{"id":13737332,"url":"https://github.com/WeiHongLee/Awesome-Multi-Task-Learning","last_synced_at":"2025-05-08T13:33:44.723Z","repository":{"id":41389659,"uuid":"439351225","full_name":"WeiHongLee/Awesome-Multi-Task-Learning","owner":"WeiHongLee","description":"An up-to-date list of works on Multi-Task 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Lists"],"sub_categories":["TeX Lists"],"readme":"# Awesome Multi-task Learning\n\nFeel free to contact me or contribute if you find any interesting paper is missing!\n\n## Table of Contents\n\n- [Survey \u0026 Study](#survey--study)\n- [Benchmarks \u0026 Code](#benchmarks--code)\n- [Papers](#papers)\n- [Awesome Multi-domain Multi-task Learning](#awesome-multi-domain-multi-task-learning)\n- [Workshops](#workshops)\n- [Online Courses](#online-courses)\n- [Related awesome list](#related-awesome-list)\n\n## Survey \u0026 Study\n\n* Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras (arXiv, 2024) [[paper](https://arxiv.org/pdf/2404.18961)] [[code](https://github.com/junfish/Awesome-Multitask-Learning)]\n\n* A Survey on Mixture of Experts  (arXiv, 2024) [[paper](https://arxiv.org/pdf/2407.06204)] [[code](https://github.com/withinmiaov/A-Survey-on-Mixture-of-Experts)]\n\n* Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types (TPAMI, 2022) [[paper](https://arxiv.org/pdf/2103.13318.pdf)]\n\n* Multi-Task Learning for Dense Prediction Tasks: A Survey (TPAMI, 2021) [[paper](https://arxiv.org/abs/2004.13379)] [[code](https://github.com/SimonVandenhende/Multi-Task-Learning-PyTorch)]\n\n* A Survey on Multi-Task Learning (TKDE, 2021) [[paper](https://ieeexplore.ieee.org/abstract/document/9392366)]\n\n* Multi-Task Learning with Deep Neural Networks: A Survey (arXiv, 2020) [[paper](http://arxiv.org/abs/2009.09796)]\n\n* Taskonomy: Disentangling Task Transfer Learning (CVPR, 2018, **Best Paper**) [[paper](https://openaccess.thecvf.com/content_cvpr_2018/papers/Zamir_Taskonomy_Disentangling_Task_CVPR_2018_paper.pdf)] [[dataset](http://taskonomy.stanford.edu/)]\n\n* A Comparison of Loss Weighting Strategies for Multi task Learning in Deep Neural Networks (IEEE Access, 2019) [[paper](https://ieeexplore.ieee.org/document/8848395)]\n\n* An Overview of Multi-Task Learning in Deep Neural Networks (arXiv, 2017) [[paper](http://arxiv.org/abs/1706.05098)]\n\n## Benchmarks \u0026 Code\n\u003cdetails\u003e\n  \u003csummary\u003eBenchmarks\u003c/summary\u003e\n\n### Dense Prediction Tasks\n\n* **[NYUv2]** Indoor Segmentation and Support Inference from RGBD Images (ECCV, 2012) [[paper](https://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf)] [[dataset](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html)]\n\n* **[Cityscapes]** The Cityscapes Dataset for Semantic Urban Scene Understanding (CVPR, 2016) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=7780719)] [[dataset](https://www.cityscapes-dataset.com/)]\n\n* **[PASCAL-Context]** The Role of Context for Object Detection and Semantic Segmentation in the Wild (CVPR, 2014) [[paper](https://cs.stanford.edu/~roozbeh/pascal-context/mottaghi_et_al_cvpr14.pdf)] [[dataset](https://cs.stanford.edu/~roozbeh/pascal-context/)]\n\n* **[Taskonomy]** Taskonomy: Disentangling Task Transfer Learning (CVPR, 2018 [best paper]) [[paper](https://openaccess.thecvf.com/content_cvpr_2018/papers/Zamir_Taskonomy_Disentangling_Task_CVPR_2018_paper.pdf)] [[dataset](http://taskonomy.stanford.edu/)]\n\n* **[KITTI]** Vision meets robotics: The KITTI dataset (IJRR, 2013) [[paper](http://www.cvlibs.net/publications/Geiger2013IJRR.pdf)] [dataset](http://www.cvlibs.net/datasets/kitti/)\n\n* **[SUN RGB-D]** SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite (CVPR 2015) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=7298655)] [[dataset](https://rgbd.cs.princeton.edu)]\n\n* **[BDD100K]** BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning (CVPR, 2020) [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Yu_BDD100K_A_Diverse_Driving_Dataset_for_Heterogeneous_Multitask_Learning_CVPR_2020_paper.pdf)] [[dataset](https://bdd-data.berkeley.edu/)]\n\n* **[Omnidata]** Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans (ICCV, 2021) [[paper](https://arxiv.org/pdf/2110.04994.pdf)] [[project](https://omnidata.vision)]\n\n* **Cityscapes-3D** Joint 2D-3D Multi-task Learning on Cityscapes-3D: 3D Detection, Segmentation, and Depth Estimation. [[dataset and code](https://github.com/prismformore/Multi-Task-Transformer/tree/main/TaskPrompter)]\n\n### Image Classification\n\n* **[Meta-dataset]** Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples (ICLR, 2020) [[paper](https://openreview.net/pdf?id=rkgAGAVKPr)] [[dataset](https://github.com/google-research/meta-dataset)]\n\n* **[Visual Domain Decathlon]** Learning multiple visual domains with residual adapters (NeurIPS, 2017) [[paper](https://arxiv.org/abs/1705.08045)] [[dataset](https://www.robots.ox.ac.uk/~vgg/decathlon/)]\n\n* **[CelebA]** Deep Learning Face Attributes in the Wild (ICCV, 2015) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=7410782)] [[dataset](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)]\n\u003c/details\u003e\n\n\n\u003cdetails\u003e\n  \u003csummary\u003eCode\u003c/summary\u003e\n\n* [[Multi-Task-Transformer](https://github.com/prismformore/Multi-Task-Transformer)]: Transformer for Multi-task Learning including dense prediction problems and 3D detection on Cityscapes.\n\n* [[Multi-Task-Learning-PyTorch](https://github.com/SimonVandenhende/Multi-Task-Learning-PyTorch)]: Multi-task Dense Prediction.\n\n* [[Auto-λ](https://github.com/lorenmt/auto-lambda)]: Multi-task Dense Prediction, Robotics.\n\n* [[UniversalRepresentations](https://github.com/VICO-UoE/UniversalRepresentations)]: [Multi-task Dense Prediction](https://github.com/VICO-UoE/UniversalRepresentations/tree/main/DensePred) (including different loss weighting strategies), [Multi-domain Classification](https://github.com/VICO-UoE/UniversalRepresentations/tree/main/VisualDecathlon), [Cross-domain Few-shot Learning](https://github.com/VICO-UoE/URL).\n\n* [[MTAN](https://github.com/lorenmt/mtan)]: Multi-task Dense Prediction, Multi-domain Classification.\n\n* [[ASTMT](https://github.com/facebookresearch/astmt)]: Multi-task Dense Prediction.\n\n* [[LibMTL](https://github.com/median-research-group/libmtl)]: Multi-task Dense Prediction.\n\n* [[MTPSL](https://github.com/VICO-UoE/MTPSL)]: Multi-task Partially-supervised Learning for Dense Prediction.\n\n* [[Resisual Adapater](https://github.com/srebuffi/residual_adapters)]: Multi-domain Classification.\n\u003c/details\u003e\n\n## Papers\n\n### 2024\n* MTMamba: Enhancing Multi-Task Dense Scene Understanding by Mamba-Based Decoders (ECCV, 2024) [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/08907.pdf)] [[code](https://github.com/EnVision-Research/MTMamba)]\n  \n* Learning Representation for Multitask Learning through Self-Supervised Auxiliary Learning (ECCV, 2024) [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/10369.pdf)]\n\n* Fair Resource Allocation in Multi-Task Learning (ICML, 2024) [[paper](https://arxiv.org/abs/2402.15638)] [[code](https://github.com/OptMN-Lab/fairgrad)]\n  \n* Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning (ICML, 2024) [[paper](https://arxiv.org/pdf/2402.04005)] [[code](https://github.com/ssi-research/BayesAgg_MTL)]\n\n* Region-aware Distribution Contrast: A Novel Approach to Multi-Task Partially Supervised Learning (arXiv, 2024) [[paper](https://arxiv.org/pdf/2403.10252.pdf)]\n\n* Multi-Task Dense Prediction via Mixture of Low-Rank Experts (CVPR, 2024) [[paper](https://arxiv.org/abs/2403.17749)] [[code](https://github.com/YuqiYang213/MLoRE)]\n\n* Joint-Task Regularization for Partially Labeled Multi-Task Learning (CVPR, 2024) [[paper](https://arxiv.org/pdf/2404.01976v1.pdf)] [[code](https://github.com/KentoNishi/JTR-CVPR-2024)]\n  \n* MTLoRA: Low-Rank Adaptation Approach for Efficient Multi-Task Learning (CVPR, 2024) [[paper](https://arxiv.org/pdf/2403.20320)] [[code](https://github.com/scale-lab/MTLoRA)]\n\n* FedHCA2: Towards Hetero-Client Federated Multi-Task Learning (CVPR, 2024) [[paper](https://arxiv.org/pdf/2311.13250)] [[code](https://github.com/innovator-zero/FedHCA2)]\n\n* Going Beyond Multi-Task Dense Prediction with Synergy Embedding Models (CVPR, 2024) [[paper](https://openaccess.thecvf.com/content/CVPR2024/papers/Huang_Going_Beyond_Multi-Task_Dense_Prediction_with_Synergy_Embedding_Models_CVPR_2024_paper.pdf)]\n  \n* Efficient Multitask Dense Predictor via Binarization (CVPR, 2024) [[paper](https://openaccess.thecvf.com/content/CVPR2024/papers/Shang_Efficient_Multitask_Dense_Predictor_via_Binarization_CVPR_2024_paper.pdf)]\n\n* DiffusionMTL: Learning Multi-Task Denoising Diffusion Model from Partially Annotated Data (CVPR, 2024) [[paper](https://arxiv.org/abs/2403.15389)] [[code](https://github.com/prismformore/DiffusionMTL)]\n\n* Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning (arXiv, 2024) [[paper](https://arxiv.org/pdf/2402.04005.pdf)] [[code](https://github.com/ssi-research/BayesAgg_MTL)]\n\n* Representation Surgery for Multi-Task Model Merging (arXiv, 2024) [[paper](https://arxiv.org/pdf/2402.02705.pdf)] [[code](https://github.com/EnnengYang/RepresentationSurgery)]\n\n* Multi-task Learning with 3D-Aware Regularization (ICLR, 2024) [[paper](https://openreview.net/attachment?id=TwBY17Hgiy\u0026name=pdf)] [[code](https://github.com/VICO-UoE/MTPSL)]\n\n* AdaMerging: Adaptive Model Merging for Multi-Task Learning (ICLR, 2024) [[paper](https://openreview.net/attachment?id=nZP6NgD3QY\u0026name=pdf)] [[code](https://github.com/EnnengYang/AdaMerging)]\n  \n* Merging Multi-Task Models via Weight-Ensembling Mixture of Experts (ICLR, 2024) [[paper](https://openreview.net/pdf?id=nLRKnO74RB)]\n  \n* ZipIt! Merging Models from Different Tasks without Training (ICLR, 2024) [[paper](https://openreview.net/attachment?id=LEYUkvdUhq\u0026name=pdf)] [[code](https://github.com/gstoica27/ZipIt)]\n\n* Denoising Task Routing for Diffusion Models (ICLR, 2024) [[paper](https://openreview.net/attachment?id=MY0qlcFcUg\u0026name=pdf)] [[code](https://byeongjun-park.github.io/DTR/)]\n\n* Active Learning with Task Consistency and Diversity in Multi-Task Networks (WACV, 2024) [[paper](https://openaccess.thecvf.com/content/WACV2024/papers/Hekimoglu_Active_Learning_With_Task_Consistency_and_Diversity_in_Multi-Task_Networks_WACV_2024_paper.pdf)] [[code](https://github.com/aralhekimoglu/mtal)]\n\n### 2023\n\n* Direction-oriented Multi-objective Learning: Simple and Provable Stochastic Algorithms (Neurips, 2023) [[paper](https://arxiv.org/abs/2305.18409)] [[code](https://github.com/OptMN-Lab/sdmgrad)]\n\n* Addressing Negative Transfer in Diffusion Models (Neurips, 2023) [[paper](https://openreview.net/pdf?id=3G2ec833mW)] [[code](https://github.com/gohyojun15/ANT_diffusion)]\n\n* Rethinking of Feature Interaction for Multi-task Learning on Dense Prediction (arXiv, 2023) [[paper](https://arxiv.org/pdf/2312.13514.pdf)]\n\n* PolyMaX: General Dense Prediction with Mask Transformer (arXiv, 2023) [[paper](https://arxiv.org/pdf/2311.05770.pdf)] [[code](https://github.com/google-research/deeplab2)]\n\n* Challenging Common Assumptions in Multi-task Learning (arXiv, 2023) [[paper](https://arxiv.org/pdf/2311.04698.pdf)]\n\n* Data exploitation: multi-task learning of object detection and semantic segmentation on partially annotated data (BMVC, 2023) [[paper](https://arxiv.org/pdf/2311.04040.pdf)] [[code](https://github.com/lhoangan/multas)]\n\n* Factorized Tensor Networks for Multi-task and Multi-domain Learning (arXiv, 2023) [[paper](https://arxiv.org/pdf/2310.06124.pdf)]\n\n* UMT-Net: A Uniform Multi-Task Network with Adaptive Task Weighting (TIV, 2023) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10264163\u0026casa_token=M3FwWSHrnG8AAAAA:lgQdSxiw05Xt5enCrn9wWxCoxxn40vmtkdw_U3gdoqmCjN_ge36-iDWScvODpvLWck6zx1VlyQQ?tag=1)]\n\n* Label Budget Allocation in Multi-Task Learning (arXiv, 2023) [[paper](https://arxiv.org/pdf/2308.12949.pdf)]\n\n* Efficient Controllable Multi-Task Architectures (arXiv, 2023) [[paper](https://arxiv.org/pdf/2308.11744.pdf)]\n\n* Foundation Model is Efficient Multimodal Multitask Model Selector (arXiv, 2023) [[paper](https://arxiv.org/abs/2308.06262)] [[code](https://github.com/OpenGVLab/Multitask-Model-Selector)]\n\n* Deformable Mixer Transformer with Gating for Multi-Task Learning of Dense Prediction (arXiv, 2023) [[paper](https://arxiv.org/abs/2308.05721)] [[code](https://github.com/yangyangxu0/DeMTG)]\n\n* AdaMV-MoE: Adaptive Multi-Task Vision Mixture-of-Experts (ICCV, 2023) [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Chen_AdaMV-MoE_Adaptive_Multi-Task_Vision_Mixture-of-Experts_ICCV_2023_paper.pdf)] [[code](https://github.com/google-research/google-research/tree/master/moe_mtl)]\n\n* Deep Multitask Learning with Progressive Parameter Sharing (ICCV, 2023) [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Shi_Deep_Multitask_Learning_with_Progressive_Parameter_Sharing_ICCV_2023_paper.pdf)]\n\n* Achievement-based Training Progress Balancing for Multi-Task Learning (ICCV, 2023) [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Yun_Achievement-Based_Training_Progress_Balancing_for_Multi-Task_Learning_ICCV_2023_paper.pdf)] [[code](https://github.com/samsung/Achievement-based-MTL)]\n\n* Multi-Task Learning with Knowledge Distillation for Dense Prediction (ICCV, 2023) [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Xu_Multi-Task_Learning_with_Knowledge_Distillation_for_Dense_Prediction_ICCV_2023_paper.pdf)]\n\n* Vision Transformer Adapters for Generalizable Multitask Learning (ICCV, 2023) [[paper](https://arxiv.org/abs/2308.12372)] [[code](https://ivrl.github.io/VTAGML/)]\n\n* TaskExpert: Dynamically Assembling Multi-Task Representations with Memorial Mixture-of-Experts (ICCV, 2023) [[paper](https://arxiv.org/pdf/2307.15324.pdf)] \n\n* Prompt Guided Transformer for Multi-Task Dense Prediction (arXiv, 2023) [[paper](https://arxiv.org/pdf/2307.15362.pdf)]\n\n* Auxiliary Learning as an Asymmetric Bargaining Game (ICML, 2023) [[paper](https://arxiv.org/pdf/2301.13501.pdf)] [[code](https://github.com/AvivSham/auxinash)]\n\n* Learning to Modulate pre-trained Models in RL (arXiv, 2023) [[paper](https://arxiv.org/abs/2306.14884)] [[code](https://github.com/ml-jku/L2M)]\n\n* **[InvPT++]**: Inverted Pyramid Multi-Task Transformer for Visual Scene Understanding (arXiv, 2023) [[paper](https://arxiv.org/pdf/2306.04842.pdf)] [[code](https://github.com/prismformore/Multi-Task-Transformer/tree/main/InvPT)]\n\n* FAMO: Fast Adaptive Multitask Optimization (arXiv, 2023) [[paper](https://arxiv.org/pdf/2306.03792.pdf)] [[code](https://github.com/Cranial-XIX/FAMO)]\n\n* Sample-Level Weighting for Multi-Task Learning with Auxiliary Tasks (arXiv, 2023) [[paper](https://arxiv.org/pdf/2306.04519.pdf)]\n\n* DynaShare: Task and Instance Conditioned Parameter Sharing for Multi-Task Learning (arXiv, 2023) [[paper](https://arxiv.org/abs/2305.17305)]\n\n* Planning-oriented Autonomous Driving (CVPR, 2023, **Best Paper**) [[paper](https://arxiv.org/pdf/2212.10156.pdf)] [[code](https://github.com/OpenDriveLab/UniAD)]\n\n* MDL-NAS: A Joint Multi-domain Learning Framework for Vision Transformer (CVPR, 2023) [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_MDL-NAS_A_Joint_Multi-Domain_Learning_Framework_for_Vision_Transformer_CVPR_2023_paper.pdf)]\n\n* Hierarchical Prompt Learning for Multi-Task Learning (CVPR, 2023) [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Hierarchical_Prompt_Learning_for_Multi-Task_Learning_CVPR_2023_paper.pdf)]\n\n* Independent Component Alignment for Multi-Task Learning (CVPR, 2023) [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Senushkin_Independent_Component_Alignment_for_Multi-Task_Learning_CVPR_2023_paper.pdf)] [[code](https://github.com/SamsungLabs/MTL)]\n\n* ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning (TMLR, 2023) [[paper](https://arxiv.org/abs/2301.12618)] [[code]()]\n\n* MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning (arXiv, 2023) [[paper](https://arxiv.org/abs/2305.07815)]\n\n* ESSR: Evolving Sparse Sharing Representation for Multi-task Learning (arXiv, 2023) [[paper](https://ieeexplore.ieee.org/abstract/document/10114675)]\n\n* AutoTaskFormer: Searching Vision Transformers for Multi-task Learning (arXiv, 2023) [[paper](https://arxiv.org/pdf/2304.08756.pdf)]\n\n* AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations (arXiv, 2023) [[paper](https://arxiv.org/pdf/2304.04959.pdf)]\n\n* A Study of Autoregressive Decoders for Multi-Tasking in Computer Vision (arXiv, 2023) [[paper](https://arxiv.org/pdf/2303.17376.pdf)]\n\n* Efficient Computation Sharing for Multi-Task Visual Scene Understanding (arXiv, 2023) [[paper](https://arxiv.org/pdf/2303.09663.pdf)]\n\n* Mod-Squad: Designing Mixture of Experts As Modular Multi-Task Learners (CVPR, 2023) [[paper](https://arxiv.org/pdf/2212.08066.pdf)] [[code](https://vis-www.cs.umass.edu/mod-squad/)]\n\n* Mitigating Task Interference in Multi-Task Learning via Explicit Task Routing with Non-Learnable Primitives (CVPR, 2023) [[paper](http://hal.cse.msu.edu/assets/pdfs/papers/2023-cvpr-multi-task-learning-non-learnable-task-routing.pdf)] [[code](https://github.com/zhichao-lu/etr-nlp-mtl)]\n\n* Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Approach (ICLR, 2023) [[paper](https://openreview.net/forum?id=dLAYGdKTi2)] \n\n* UNIVERSAL FEW-SHOT LEARNING OF DENSE PREDIC- TION TASKS WITH VISUAL TOKEN MATCHING (ICLR, 2023) [[paper](https://openreview.net/pdf?id=88nT0j5jAn)]\n\n* TASKPROMPTER: SPATIAL-CHANNEL MULTI-TASK PROMPTING FOR DENSE SCENE UNDERSTANDING (ICLR, 2023) [[paper](https://openreview.net/forum?id=-CwPopPJda)] [[code](https://github.com/prismformore/Multi-Task-Transformer/tree/main/TaskPrompter)] [[dataset](https://arxiv.org/pdf/2304.00971.pdf)]\n\n* Contrastive Multi-Task Dense Prediction (AAAI 2023) [[paper](https://laos-y.github.io/uploads/yang2023AAAI/2437.YangS.pdf)]\n\n* Composite Learning for Robust and Effective Dense Predictions (WACV, 2023) [[paper](https://arxiv.org/abs/2210.07239)]\n\n* Toward Edge-Efficient Dense Predictions with Synergistic Multi-Task Neural Architecture Search (WACV, 2023) [[paper](https://arxiv.org/abs/2210.01384)] \n\n* Cross-task Attention Mechanism for Dense Multi-task Learning (WACV, 2023) [[paper](https://openaccess.thecvf.com/content/WACV2023/papers/Lopes_Cross-Task_Attention_Mechanism_for_Dense_Multi-Task_Learning_WACV_2023_paper.pdf)] [[code](https://github.com/astra-vision/DenseMTL)]\n\n### 2022\n\n* RepMode: Learning to Re-parameterize Diverse Experts for Subcellular Structure Prediction (arXiv, 2022) [[paper](https://arxiv.org/abs/2212.10066)]\n\n* LEARNING USEFUL REPRESENTATIONS FOR SHIFTING TASKS AND DISTRIBUTIONS (arXiv, 2022) [[paper](https://arxiv.org/abs/2212.07346)]\n\n* Sub-Task Imputation via Self-Labelling to Train Image Moderation Models on Sparse Noisy Data (ACM CIKM, 2022) [[paper](https://dl.acm.org/doi/pdf/10.1145/3511808.3557149)]\n\n* Multi-Task Meta Learning: learn how to adapt to unseen tasks (arXiv, 2022) [[paper](https://arxiv.org/pdf/2210.06989.pdf)]\n\n* M\u003csup\u003e3\u003c/sup\u003eViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design (NeurIPS, 2022) [[paper](https://openreview.net/pdf?id=cFOhdl1cyU-)] [[code](https://github.com/VITA-Group/M3ViT)]\n\n* AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning (NeurIPS, 2022) [[paper](https://arxiv.org/abs/2110.13076)] [[code](https://github.com/zhanglijun95/AutoMTL)]\n\n* Association Graph Learning for Multi-Task Classification with Category Shifts (NeurIPS, 2022) [[paper](https://arxiv.org/pdf/2210.04637.pdf)] [[code](https://github.com/autumn9999/MTC-with-Category-Shifts)]\n\n* Do Current Multi-Task Optimization Methods in Deep Learning Even Help? (NeurIPS, 2022) [[paper](https://arxiv.org/abs/2209.11379)]\n\n* Task Discovery: Finding the Tasks that Neural Networks Generalize on (NeurIPS, 2022) [[paper](https://taskdiscovery.epfl.ch/static/paper/arxiv.pdf)]\n\n* **[Auto-λ]** Auto-λ: Disentangling Dynamic Task Relationships (TMLR, 2022) [[paper](https://arxiv.org/pdf/2202.03091.pdf)] [[code](https://github.com/lorenmt/auto-lambda)]\n\n* **[Universal Representations]** Universal Representations: A Unified Look at Multiple Task and Domain Learning (arXiv, 2022) [[paper](https://arxiv.org/pdf/2204.02744.pdf)] [[code](https://github.com/VICO-UoE/UniversalRepresentations)]\n\n* MTFormer: Multi-Task Learning via Transformer and Cross-Task Reasoning (ECCV, 2022) [[paper](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136870299.pdf)] \n\n* Not All Models Are Equal: Predicting Model Transferability in a Self-challenging Fisher Space (ECCV, 2022) [[paper](https://arxiv.org/abs/2207.03036)] [[code](https://github.com/TencentARC/SFDA)]\n\n* Factorizing Knowledge in Neural Networks (ECCV, 2022) [[paper](https://arxiv.org/abs/2207.03337)] [[code](https://github.com/Adamdad/KnowledgeFactor)]\n\n* **[InvPT]** Inverted Pyramid Multi-task Transformer for Dense Scene Understanding (ECCV, 2022) [[paper](https://arxiv.org/pdf/2203.07997.pdf)] [[code](https://github.com/prismformore/InvPT)]\n\n* **[MultiMAE]** MultiMAE: Multi-modal Multi-task Masked Autoencoders (ECCV, 2022) [[paper](https://arxiv.org/pdf/2204.01678.pdf)] [[code](https://multimae.epfl.ch)]\n\n* A Multi-objective / Multi-task Learning Framework Induced by Pareto Stationarity (ICML, 2022) [[paper](https://proceedings.mlr.press/v162/momma22a.html)]\n\n* Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization (ICML, 2022) [[paper](https://proceedings.mlr.press/v162/javaloy22a.html)]\n\n* Active Multi-Task Representation Learning (ICML, 2022) [[paper](https://proceedings.mlr.press/v162/chen22j.html)]\n\n* Generative Modeling for Multi-task Visual Learning (ICML, 2022) [[paper](https://proceedings.mlr.press/v162/bao22c.html)] [[code](https://github.com/zpbao/multi-task-oriented_generative_modeling)]\n\n* Multi-Task Learning as a Bargaining Game (ICML, 2022) [[paper](https://proceedings.mlr.press/v162/navon22a.html)] [[code](https://github.com/AvivNavon/nash-mtl)]\n\n* Multi-Task Learning with Multi-query Transformer for Dense Prediction (arXiv, 2022) [[paper](https://arxiv.org/pdf/2205.14354.pdf)]\n\n* **[Gato]** A Generalist Agent (arXiv, 2022) [[paper](https://arxiv.org/pdf/2205.06175.pdf)]\n\n* **[MTPSL]** Learning Multiple Dense Prediction Tasks from Partially Annotated Data (CVPR, 2022, **Best Paper Finalist**) [[paper](https://arxiv.org/pdf/2111.14893.pdf)] [[code](https://github.com/VICO-UoE/MTPSL)]\n\n* **[TSA]** Cross-domain Few-shot Learning with Task-specific Adapters (CVPR, 2022) [[paper](https://arxiv.org/pdf/2107.00358.pdf)] [[code](https://github.com/VICO-UoE/URL)]\n\n* **[OMNIVORE]** OMNIVORE: A Single Model for Many Visual Modalities (CVPR, 2022) [[paper](https://arxiv.org/pdf/2201.08377.pdf)] [[code](https://github.com/facebookresearch/omnivore)]\n\n* Task Adaptive Parameter Sharing for Multi-Task Learning (CVPR, 2022) [[paper](https://arxiv.org/pdf/2203.16708.pdf)]\n\n* Controllable Dynamic Multi-Task Architectures (CVPR, 2022) [[paper](https://arxiv.org/pdf/2203.14949.pdf)] [[code](https://www.nec-labs.com/~mas/DYMU/)]\n\n* **[SHIFT]** SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation (CVPR, 2022) [[paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_SHIFT_A_Synthetic_Driving_Dataset_for_Continuous_Multi-Task_Domain_Adaptation_CVPR_2022_paper.pdf)] [[code](https://www.vis.xyz/shift/)]\n\n* DiSparse: Disentangled Sparsification for Multitask Model Compression (CVPR, 2022) [[paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_DiSparse_Disentangled_Sparsification_for_Multitask_Model_Compression_CVPR_2022_paper.pdf)] [[code](https://github.com/SHI-Labs/DiSparse-Multitask-Model-Compression)]\n\n* **[MulT]** MulT: An End-to-End Multitask Learning Transformer (CVPR, 2022) [[paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Bhattacharjee_MulT_An_End-to-End_Multitask_Learning_Transformer_CVPR_2022_paper.pdf)] [[code](https://github.com/IVRL/MulT)]\n\n* Sound and Visual Representation Learning with Multiple Pretraining Tasks (CVPR, 2022) [[paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Vasudevan_Sound_and_Visual_Representation_Learning_With_Multiple_Pretraining_Tasks_CVPR_2022_paper.pdf)]\n\n* Medusa: Universal Feature Learning via Attentional Multitasking (CVPR Workshop, 2022) [[paper](https://arxiv.org/abs/2204.05698)]\n\n* An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems (arXiv, 2022) [[paper](https://arxiv.org/pdf/2205.12755.pdf)] [[code](https://github.com/google-research/google-research/tree/master/muNet)]\n\n* Combining Modular Skills in Multitask Learning (arXiv, 2022) [[paper](https://arxiv.org/pdf/2202.13914.pdf)]\n\n* Visual Representation Learning over Latent Domains (ICLR, 2022) [[paper](https://openreview.net/pdf?id=kG0AtPi6JI1)]\n\n* ADARL: What, Where, and How to Adapt in Transfer Reinforcement Learning (ICLR, 2022) [[paper](https://openreview.net/pdf?id=8H5bpVwvt5)] [[code](https://github.com/Adaptive-RL/AdaRL-code)]\n\n* Towards a Unified View of Parameter-Efficient Transfer Learning (ICLR, 2022) [[paper](https://openreview.net/pdf?id=0RDcd5Axok)] [[code](https://github.com/jxhe/unify-parameter-efficient-tuning)]\n\n* **[Rotograd]** Rotograd: Dynamic Gradient Homogenization for Multi-Task Learning (ICLR, 2022) [[paper](https://openreview.net/pdf?id=T8wHz4rnuGL)] [[code](https://github.com/adrianjav/rotograd)]\n\n* Relational Multi-task Learning: Modeling Relations Between Data and Tasks (ICLR, 2022) [[paper](https://openreview.net/pdf?id=8Py-W8lSUgy)]\n\n* Weighted Training for Cross-task Learning (ICLR, 2022) [[paper](https://openreview.net/pdf?id=ltM1RMZntpu)] [[code](https://github.com/CogComp/TAWT)]\n\n* Semi-supervised Multi-task Learning for Semantics and Depth (WACV, 2022) [[paper](https://openaccess.thecvf.com/content/WACV2022/papers/Wang_Semi-Supervised_Multi-Task_Learning_for_Semantics_and_Depth_WACV_2022_paper.pdf)]\n\n* In Defense of the Unitary Scalarization for Deep Multi-Task Learning (arXiv, 2022) [[paper](https://arxiv.org/pdf/2201.04122.pdf)]\n\n### 2021\n\n* Variational Multi-Task Learning with Gumbel-Softmax Priors (NeurIPS, 2021) [[paper](https://arxiv.org/pdf/2111.05323.pdf)] [[code](https://github.com/autumn9999/VMTL)]\n\n* Efficiently Identifying Task Groupings for Multi-Task Learning (NeurIPS, 2021) [[paper](http://arxiv.org/abs/2109.04617)]\n\n* **[CAGrad]** Conflict-Averse Gradient Descent for Multi-task Learning (NeurIPS, 2021) [[paper](https://openreview.net/pdf?id=_61Qh8tULj_)] [[code](https://github.com/Cranial-XIX/CAGrad)]\n\n* A Closer Look at Loss Weighting in Multi-Task Learning (arXiv, 2021) [[paper](https://arxiv.org/pdf/2111.10603.pdf)]\n\n* Exploring Relational Context for Multi-Task Dense Prediction (ICCV, 2021) [[paper](http://arxiv.org/abs/2104.13874)] [[code](https://github.com/brdav/atrc)]\n\n* Multi-Task Self-Training for Learning General Representations (ICCVW, 2021) [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Ghiasi_Multi-Task_Self-Training_for_Learning_General_Representations_ICCV_2021_paper.pdf)]\n\n* Task Switching Network for Multi-task Learning (ICCV, 2021) [[paper](https://openaccess.thecvf.com/content/ICCV2021/html/Sun_Task_Switching_Network_for_Multi-Task_Learning_ICCV_2021_paper.html)] [[code](https://github.com/GuoleiSun/TSNs)]\n\n* Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans (ICCV, 2021) [[paper](https://arxiv.org/pdf/2110.04994.pdf)] [[project](https://omnidata.vision)]\n\n* Robustness via Cross-Domain Ensembles (ICCV, 2021) [[paper](https://arxiv.org/abs/2103.10919)] [[code](https://github.com/EPFL-VILAB/XDEnsembles)]\n\n* Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation (ICCV, 2021) [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_Domain_Adaptive_Semantic_Segmentation_With_Self-Supervised_Depth_Estimation_ICCV_2021_paper.pdf)] [[code](https://qin.ee/corda)]\n\n* **[URL]** Universal Representation Learning from Multiple Domains for Few-shot Classification (ICCV, 2021) [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Li_Universal_Representation_Learning_From_Multiple_Domains_for_Few-Shot_Classification_ICCV_2021_paper.pdf)] [[code](https://github.com/VICO-UoE/URL)]\n\n* **[tri-M]** A Multi-Mode Modulator for Multi-Domain Few-Shot Classification (ICCV, 2021) [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Liu_A_Multi-Mode_Modulator_for_Multi-Domain_Few-Shot_Classification_ICCV_2021_paper.pdf)] [[code](https://github.com/csyanbin/tri-M-ICCV)]\n\n* MultiTask-CenterNet (MCN): Efficient and Diverse Multitask Learning using an Anchor Free Approach (ICCV Workshop, 2021) [[paper](https://openaccess.thecvf.com/content/ICCV2021W/ERCVAD/papers/Heuer_MultiTask-CenterNet_MCN_Efficient_and_Diverse_Multitask_Learning_Using_an_Anchor_ICCVW_2021_paper.pdf)]\n\n* See Yourself in Others: Attending Multiple Tasks for Own Failure Detection (arXiv, 2021) [[paper](https://arxiv.org/pdf/2110.02549.pdf)]\n\n* A Multi-Task Cross-Task Learning Architecture for Ad-hoc Uncertainty Estimation in 3D Cardiac MRI Image Segmentation (CinC, 2021) [[paper](https://www.cinc.org/2021/Program/accepted/115_Preprint.pdf)] [[code](https://github.com/SMKamrulHasan/MTCTL)]\n\n* Multi-Task Reinforcement Learning with Context-based Representations (ICML, 2021) [[paper](http://arxiv.org/abs/2102.06177)]\n\n* **[FLUTE]** Learning a Universal Template for Few-shot Dataset Generalization (ICML, 2021) [[paper](https://arxiv.org/pdf/2105.07029.pdf)] [[code](https://github.com/google-research/meta-dataset)]\n\n* Towards a Unified View of Parameter-Efficient Transfer Learning (arXiv, 2021) [[paper](http://arxiv.org/abs/2110.04366)]\n\n* UniT: Multimodal Multitask Learning with a Unified Transformer (arXiv, 2021) [[paper](http://arxiv.org/abs/2102.10772)]\n\n* Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation (CVPR, 2021) [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Saha_Learning_To_Relate_Depth_and_Semantics_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.pdf)] [[code](https://github.com/susaha/ctrl-uda)]\n\n* CompositeTasking: Understanding Images by Spatial Composition of Tasks (CVPR, 2021) [[paper](https://openaccess.thecvf.com/content/CVPR2021/html/Popovic_CompositeTasking_Understanding_Images_by_Spatial_Composition_of_Tasks_CVPR_2021_paper.html)] [[code](https://github.com/nikola3794/composite-tasking)]\n\n* Anomaly Detection in Video via Self-Supervised and Multi-Task Learning (CVPR, 2021) [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Georgescu_Anomaly_Detection_in_Video_via_Self-Supervised_and_Multi-Task_Learning_CVPR_2021_paper.pdf)]\n\n* Taskology: Utilizing Task Relations at Scale (CVPR, 2021) [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Lu_Taskology_Utilizing_Task_Relations_at_Scale_CVPR_2021_paper.pdf)]\n\n* Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation (CVPR, 2021) [[paper](https://arxiv.org/pdf/2012.10782.pdf)] [[code](https://github.com/lhoyer/improving_segmentation_with_selfsupervised_depth)]\n\n* Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation (arXiv, 2021) [[paper](https://arxiv.org/pdf/2108.12545.pdf)] [[code](https://github.com/lhoyer/improving_segmentation_with_selfsupervised_depth)]\n\n* Counter-Interference Adapter for Multilingual Machine Translation (Findings of EMNLP, 2021) [[paper](https://aclanthology.org/2021.findings-emnlp.240)]\n\n* Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters \u0026 Less Data (ICLR) [[paper](https://openreview.net/forum?id=de11dbHzAMF)] [[code](https://github.com/CAMTL/CA-MTL)]\n\n* **[Gradient Vaccine]** Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models (ICLR, 2021) [[paper](https://openreview.net/forum?id=F1vEjWK-lH_)] \n\n* **[IMTL]** Towards Impartial Multi-task Learning (ICLR, 2021) [[paper](https://openreview.net/forum?id=IMPnRXEWpvr)]\n\n* Deciphering and Optimizing Multi-Task Learning: A Random Matrix Approach (ICLR, 2021) [[paper](https://openreview.net/forum?id=Cri3xz59ga)]\n\n* **[URT]** A Universal Representation Transformer Layer for Few-Shot Image Classification (ICLR, 2021) [[paper](https://arxiv.org/pdf/2006.11702.pdf)] [[code](https://github.com/liulu112601/URT)]\n\n* Flexible Multi-task Networks by Learning Parameter Allocation (ICLR Workshop, 2021) [[paper](http://arxiv.org/abs/1910.04915)]\n\n* Multi-Loss Weighting with Coefficient of Variations (WACV, 2021) [[paper](https://openaccess.thecvf.com/content/WACV2021/papers/Groenendijk_Multi-Loss_Weighting_With_Coefficient_of_Variations_WACV_2021_paper.pdf)] [[code](https://github.com/rickgroen/cov-weighting)]\n\n### 2020\n\n* Multi-Task Reinforcement Learning with Soft Modularization (NeurIPS, 2020) [[paper](http://arxiv.org/abs/2003.13661)] [[code](https://github.com/RchalYang/Soft-Module)] \n* AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning (NeurIPS, 2020) [[paper](http://arxiv.org/abs/1911.12423)] [[code](https://github.com/sunxm2357/AdaShare)]\n\n* **[GradDrop]** Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout (NeurIPS, 2020) [[paper](https://proceedings.NeurIPS.cc//paper/2020/file/16002f7a455a94aa4e91cc34ebdb9f2d-Paper.pdf)] [[code](https://github.com/tensorflow/lingvo/blob/master/lingvo/core/graddrop.py)]\n\n* **[PCGrad]** Gradient Surgery for Multi-Task Learning (NeurIPS, 2020) [[paper](http://arxiv.org/abs/2001.06782)] [[tensorflow](https://github.com/tianheyu927/PCGrad)] [[pytorch](https://github.com/WeiChengTseng/Pytorch-PCGrad)]\n\n* On the Theory of Transfer Learning: The Importance of Task Diversity (NeurIPS, 2020) [[paper](https://proceedings.NeurIPS.cc//paper/2020/file/59587bffec1c7846f3e34230141556ae-Paper.pdf)]\n\n* A Study of Residual Adapters for Multi-Domain Neural Machine Translation (WMT, 2020) [[paper](https://www.aclweb.org/anthology/2020.wmt-1.72/)]\n\n* Multi-Task Adversarial Attack (arXiv, 2020) [[paper](http://arxiv.org/abs/2011.09824)]\n\n* Automated Search for Resource-Efficient Branched Multi-Task Networks (BMVC, 2020) [[paper](http://arxiv.org/abs/2008.10292)] [[code](https://github.com/brdav/bmtas)]\n* Branched Multi-Task Networks: Deciding What Layers To Share (BMVC, 2020) [[paper](http://arxiv.org/abs/1904.02920)]\n\n* MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning (ECCV, 2020) [[paper](http://arxiv.org/abs/2001.06902)] [[code](https://github.com/SimonVandenhende/Multi-Task-Learning-PyTorch)]\n\n* Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference (ECCV, 2020) [[paper](http://arxiv.org/abs/2007.12540)] [[code](https://github.com/menelaoskanakis/RCM)]\n\n* Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification (ECCV, 2020) [[paper](https://arxiv.org/pdf/2003.09338.pdf)] [[code](https://github.com/dvornikita/SUR)]\n\n* Multitask Learning Strengthens Adversarial Robustness (ECCV 2020) [[paper](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123470154.pdf)] [[code](https://github.com/columbia/MTRobust)]\n\n* Duality Diagram Similarity: a generic framework for initialization selection in task transfer learning (ECCV, 2020) [[paper](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123710494.pdf)] [[code](https://github.com/cvai-repo/duality-diagram-similarity)]\n\n* **[KD4MTL]** Knowledge Distillation for Multi-task Learning (ECCV Workshop) [[paper](https://arxiv.org/pdf/2007.06889.pdf)] [[code](https://github.com/VICO-UoE/KD4MTL)]\n\n* MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning (CVPR, 2020) [[paper](https://arxiv.org/abs/2003.14058)] [[code](https://github.com/bhpfelix/MTLNAS)]\n\n* Robust Learning Through Cross-Task Consistency (CVPR, 2020) [[paper](https://consistency.epfl.ch/Cross_Task_Consistency_CVPR2020.pdf)] [[code](https://github.com/EPFL-VILAB/XTConsistency)]\n\n* 12-in-1: Multi-Task Vision and Language Representation Learning (CVPR, 2020) [paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Lu_12-in-1_Multi-Task_Vision_and_Language_Representation_Learning_CVPR_2020_paper.pdf) [[code](https://github.com/facebookresearch/vilbert-multi-task)]\n\n* A Multi-task Mean Teacher for Semi-supervised Shadow Detection (CVPR, 2020) [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_A_Multi-Task_Mean_Teacher_for_Semi-Supervised_Shadow_Detection_CVPR_2020_paper.pdf)] [[code](https://github.com/eraserNut/MTMT)]\n\n* MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer (EMNLP, 2020) [[paper](https://doi.org/10.18653/v1/2020.emnlp-main.617)]\n\n* Masking as an Efficient Alternative to Finetuning for Pretrained Language Models (EMNLP, 2020) [[paper](http://arxiv.org/abs/2004.12406)] [[code](https://github.com/ptlmasking/maskbert)]\n\n* Effcient Continuous Pareto Exploration in Multi-Task Learning (ICML, 2020) [[paper](http://proceedings.mlr.press/v119/ma20a/ma20a.pdf)] [[code](https://github.com/mit-gfx/ContinuousParetoMTL)]\n\n* Which Tasks Should Be Learned Together in Multi-task Learning? (ICML, 2020) [[paper](http://arxiv.org/abs/1905.07553)] [[code](https://github.com/tstandley/taskgrouping)]\n\n* Learning to Branch for Multi-Task Learning (ICML, 2020) [[paper](https://arxiv.org/abs/2006.01895)]\n\n* Partly Supervised Multitask Learning (ICMLA, 2020) [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9356271)\n\n* Understanding and Improving Information Transfer in Multi-Task Learning (ICLR, 2020) [[paper](https://arxiv.org/abs/2005.00944)]\n\n* Measuring and Harnessing Transference in Multi-Task Learning (arXiv, 2020) [[paper](https://arxiv.org/abs/2010.15413)]\n\n* Multi-Task Semi-Supervised Adversarial Autoencoding for Speech Emotion Recognition (arXiv, 2020) [[paper](https://arxiv.org/pdf/1907.06078.pdf)]\n\n* Learning Sparse Sharing Architectures for Multiple Tasks (AAAI, 2020) [[paper](http://arxiv.org/abs/1911.05034)]\n\n* AdapterFusion: Non-Destructive Task Composition for Transfer Learning (arXiv, 2020) [[paper](http://arxiv.org/abs/2005.00247)]\n\n### 2019\n\n* Adaptive Auxiliary Task Weighting for Reinforcement Learning (NeurIPS, 2019) [[paper](https://papers.nips.cc/paper/2019/hash/0e900ad84f63618452210ab8baae0218-Abstract.html)]\n\n* Pareto Multi-Task Learning (NeurIPS, 2019) [[paper](http://papers.nips.cc/paper/9374-pareto-multi-task-learning.pdf)] [[code](https://github.com/Xi-L/ParetoMTL)]\n\n* Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains (NeurIPS, 2019) [[paper](http://arxiv.org/abs/1906.00097)]\n\n* Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes (NeurIPS, 2019) [[paper](https://github.com/cambridge-mlg/cnaps)] [[code](https://proceedings.neurips.cc/paper/2019/file/1138d90ef0a0848a542e57d1595f58ea-Paper.pdf)]\n\n* **[Orthogonal]** Regularizing Deep Multi-Task Networks using Orthogonal Gradients (arXiv, 2019) [[paper](http://arxiv.org/abs/1912.06844)]\n\n* Many Task Learning With Task Routing (ICCV, 2019) [[paper](https://openaccess.thecvf.com/content_ICCV_2019/papers/Strezoski_Many_Task_Learning_With_Task_Routing_ICCV_2019_paper.pdf)] [[code](https://github.com/gstrezoski/TaskRouting)]\n\n* Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels (ICCV, 2019) [[paper](https://arxiv.org/abs/1908.09597)]\n\n* Deep Elastic Networks with Model Selection for Multi-Task Learning (ICCV, 2019) [[paper](http://arxiv.org/abs/1909.04860)] [[code](https://github.com/rllab-snu/Deep-Elastic-Network)]\n\n* Feature Partitioning for Efficient Multi-Task Architectures (arXiv, 2019) [[paper](https://arxiv.org/abs/1908.04339)] [[code](https://github.com/google/multi-task-architecture-search)]\n\n* Task Selection Policies for Multitask Learning (arXiv, 2019) [[paper](http://arxiv.org/abs/1907.06214)]\n\n* BAM! Born-Again Multi-Task Networks for Natural Language Understanding (ACL, 2019) [[paper](https://www.aclweb.org/anthology/P19-1595/)] [[code](https://github.com/google-research/google-research/tree/master/bam)]\n\n* OmniNet: A unified architecture for multi-modal multi-task learning (arXiv, 2019) [[paper](http://arxiv.org/abs/1907.07804)]\n\n* NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction (CVPR, 2019) [[paper](https://arxiv.org/abs/1801.08297)] [[code](https://github.com/ethanygao/NDDR-CNN)]\n\n* **[MTAN + DWA]** End-to-End Multi-Task Learning with Attention (CVPR, 2019) [[paper](http://arxiv.org/abs/1803.10704)] [[code](https://github.com/lorenmt/mtan)] \n\n* Attentive Single-Tasking of Multiple Tasks (CVPR, 2019) [[paper](http://arxiv.org/abs/1904.08918)] [[code](https://github.com/facebookresearch/astmt)]\n\n* Pattern-Affinitive Propagation Across Depth, Surface Normal and Semantic Segmentation (CVPR, 2019) [[paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Pattern-Affinitive_Propagation_Across_Depth_Surface_Normal_and_Semantic_Segmentation_CVPR_2019_paper.pdf)]\n\n* Representation Similarity Analysis for Efficient Task Taxonomy \u0026 Transfer Learning (CVPR, 2019) [[paper](https://arxiv.org/abs/1904.11740)] [[code](https://github.com/kshitijd20/RSA-CVPR19-release)]\n\n* **[Geometric Loss Strategy (GLS)]** MultiNet++: Multi-Stream Feature Aggregation and Geometric Loss Strategy for Multi-Task Learning (CVPR Workshop, 2019) [[paper](http://arxiv.org/abs/1904.08492)]\n\n* Parameter-Efficient Transfer Learning for NLP (ICML, 2019) [[paper](http://arxiv.org/abs/1902.00751)]\n\n* BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning (ICML, 2019) [[paper](http://arxiv.org/abs/1902.02671)] [[code](https://github.com/AsaCooperStickland/Bert-n-Pals)]\n\n* Tasks Without Borders: A New Approach to Online Multi-Task Learning (ICML Workshop, 2019) [[paper](https://openreview.net/pdf?id=HkllV5Bs24)]\n\n* AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning (NACCL, 2019) [[paper](https://arxiv.org/abs/1904.04153)] [[code](https://github.com/HanGuo97/AutoSeM)]\n\n* Multi-Task Deep Reinforcement Learning with PopArt (AAAI, 2019) [[paper](https://doi.org/10.1609/aaai.v33i01.33013796)]\n\n* SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning (AAAI, 2019) [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/3788/3666)]\n\n* Latent Multi-task Architecture Learning (AAAI, 2019) [[paper](https://arxiv.org/abs/1705.08142)] [[code](https://github.com/ sebastianruder/sluice-networks)]\n\n* Multi-Task Deep Neural Networks for Natural Language Understanding (ACL, 2019) [[paper](https://arxiv.org/pdf/1901.11504.pdf)]\n\n### 2018\n\n* Learning to Multitask (NeurIPS, 2018) [[paper](https://papers.nips.cc/paper/2018/file/aeefb050911334869a7a5d9e4d0e1689-Paper.pdf)]\n\n* **[MGDA]** Multi-Task Learning as Multi-Objective Optimization (NeurIPS, 2018) [[paper](http://arxiv.org/abs/1810.04650)] [[code](https://github.com/isl-org/MultiObjectiveOptimization)]\n\n* Adapting Auxiliary Losses Using Gradient Similarity (arXiv, 2018) [[paper](http://arxiv.org/abs/1812.02224)] [[code](https://github.com/szkocot/Adapting-Auxiliary-Losses-Using-Gradient-Similarity)]\n\n* Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights (ECCV, 2018) [[paper](https://openaccess.thecvf.com/content_ECCV_2018/papers/Arun_Mallya_Piggyback_Adapting_a_ECCV_2018_paper.pdf)] [[code](https://github.com/arunmallya/piggyback)]\n\n* Dynamic Task Prioritization for Multitask Learning (ECCV, 2018) [[paper](https://openaccess.thecvf.com/content_ECCV_2018/papers/Michelle_Guo_Focus_on_the_ECCV_2018_paper.pdf)]\n\n* A Modulation Module for Multi-task Learning with Applications in Image Retrieval (ECCV, 2018) [[paper](https://arxiv.org/abs/1807.06708)]\n\n* Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts (KDD, 2018) [[paper](https://dl.acm.org/doi/pdf/10.1145/3219819.3220007)] \n\n* Unifying and Merging Well-trained Deep Neural Networks for Inference Stage (IJCAI, 2018) [[paper](http://arxiv.org/abs/1805.04980)] [[code](https://github.com/ivclab/NeuralMerger)]\n\n* Efficient Parametrization of Multi-domain Deep Neural Networks (CVPR, 2018) [[paper](https://openaccess.thecvf.com/content_cvpr_2018/papers/Rebuffi_Efficient_Parametrization_of_CVPR_2018_paper.pdf)] [[code](https://github.com/srebuffi/residual_adapters)]\n\n* PAD-Net: Multi-tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing (CVPR, 2018) [[paper](https://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_PAD-Net_Multi-Tasks_Guided_CVPR_2018_paper.pdf)]\n\n* NestedNet: Learning Nested Sparse Structures in Deep Neural Networks (CVPR, 2018) [[paper](https://openaccess.thecvf.com/content_cvpr_2018/papers/Kim_NestedNet_Learning_Nested_CVPR_2018_paper.pdf)]\n\n* PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning (CVPR, 2018) [[paper](https://openaccess.thecvf.com/content_cvpr_2018/papers/Mallya_PackNet_Adding_Multiple_CVPR_2018_paper.pdf)] [[code](https://github.com/arunmallya/packnet)]\n\n\n* **[Uncertainty]** Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics (CVPR, 2018) [[paper](https://openaccess.thecvf.com/content_cvpr_2018/papers/Kendall_Multi-Task_Learning_Using_CVPR_2018_paper.pdf)]\n\n* Deep Asymmetric Multi-task Feature Learning (ICML, 2018) [[paper](http://proceedings.mlr.press/v80/lee18d/lee18d.pdf)]\n\n* **[GradNorm]** GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks (ICML, 2018) [[paper](http://arxiv.org/abs/1711.02257)]\n\n* Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing---and Back (ICML, 2018) [[paper](http://arxiv.org/abs/1803.04062)]\n\n* Gradient Adversarial Training of Neural Networks (arXiv, 2018) [[paper](http://arxiv.org/abs/1806.08028)]\n\n* Auxiliary Tasks in Multi-task Learning (arXiv, 2018) [[paper](http://arxiv.org/abs/1805.06334)]\n\n* Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning (ICLR, 2018) [[paper](http://arxiv.org/abs/1711.01239)] [[code](https://github.com/cle-ros/RoutingNetworks)\n\n* Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering (ICLR, 2018) [[paper](http://arxiv.org/abs/1711.00108)]\n\n### 2017\n\n* Learning multiple visual domains with residual adapters (NeurIPS, 2017) [[paper](https://papers.nips.cc/paper/2017/file/e7b24b112a44fdd9ee93bdf998c6ca0e-Paper.pdf)] [[code](https://github.com/srebuffi/residual_adapters)]\n\n* Learning Multiple Tasks with Multilinear Relationship Networks (NeurIPS, 2017) [[paper](https://proceedings.NeurIPS.cc/paper/2017/file/03e0704b5690a2dee1861dc3ad3316c9-Paper.pdf)] [[code](https://github.com/thuml/MTlearn)]\n\n* Federated Multi-Task Learning (NeurIPS, 2017) [[paper](https://proceedings.NeurIPS.cc/paper/2017/file/6211080fa89981f66b1a0c9d55c61d0f-Paper.pdf)] [[code](https://github.com/gingsmith/fmtl)]\n\n* Multi-task Self-Supervised Visual Learning (ICCV, 2017) [[paper](http://arxiv.org/abs/1708.07860)]\n\n* Adversarial Multi-task Learning for Text Classification (ACL, 2017) [[paper](http://arxiv.org/abs/1704.05742)]\n\n* UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory (CVPR, 2017) [[paper](https://arxiv.org/abs/1609.02132)]\n\n* Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification (CVPR, 2017) [[paper](https://openaccess.thecvf.com/content_cvpr_2017/papers/Lu_Fully-Adaptive_Feature_Sharing_CVPR_2017_paper.pdf)]\n\n* Modular Multitask Reinforcement Learning with Policy Sketches (ICML, 2017) [[paper](http://arxiv.org/abs/1611.01796)] [[code](https://github.com/jacobandreas/psketch)]\n\n\n* SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization (ICML, 2017) [[paper](http://proceedings.mlr.press/v70/kim17b.html)] [[code](https://github.com/dalgu90/splitnet-wrn)]\n\n* One Model To Learn Them All (arXiv, 2017) [[paper](http://arxiv.org/abs/1706.05137)] [[code](https://github.com/tensorflow/tensor2tensor)]\n\n* **[AdaLoss]** Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing (arXiv, 2017) [[paper](http://arxiv.org/abs/1708.06832)]\n\n* Deep Multi-task Representation Learning: A Tensor Factorisation Approach (ICLR, 2017) [[paper](https://arxiv.org/abs/1605.06391)] [[code](https://github.com/wOOL/DMTRL)]\n\n* Trace Norm Regularised Deep Multi-Task Learning (ICLR Workshop, 2017) [[paper](http://arxiv.org/abs/1606.04038)] [[code](https://github.com/wOOL/TNRDMTL)]\n\n* When is multitask learning effective? Semantic sequence prediction under varying data conditions (EACL, 2017) [[paper](http://arxiv.org/abs/1612.02251)] [[code](https://github.com/bplank/multitasksemantics)]\n\n* Identifying beneficial task relations for multi-task learning in deep neural networks (EACL, 2017) [[paper](http://arxiv.org/abs/1702.08303)]\n\n* PathNet: Evolution Channels Gradient Descent in Super Neural Networks (arXiv, 2017) [[paper](http://arxiv.org/abs/1701.08734)] [[code](https://github.com/jsikyoon/pathnet)]\n\n* Attributes for Improved Attributes: A Multi-Task Network Utilizing Implicit and Explicit Relationships for Facial Attribute Classification (AAAI, 2017) [[paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/viewFile/14749/14282)]\n\n### 2016 and earlier\n\n* Learning values across many orders of magnitude (NeurIPS, 2016) [[paper](https://arxiv.org/abs/1602.07714)]\n\n* Integrated Perception with Recurrent Multi-Task Neural Networks (NeurIPS, 2016) [[paper](https://proceedings.neurips.cc/paper/2016/file/06409663226af2f3114485aa4e0a23b4-Paper.pdf)] \n\n* Unifying Multi-Domain Multi-Task Learning: Tensor and Neural Network Perspectives (arXiv, 2016) [[paper](http://arxiv.org/abs/1611.09345)]\n\n* Progressive Neural Networks (arXiv, 2016) [[paper](https://arxiv.org/abs/1606.04671)]\n\n* Deep multi-task learning with low level tasks supervised at lower layers (ACL, 2016) [[paper](https://www.aclweb.org/anthology/P16-2038.pdf)]\n\n* **[Cross-Stitch]** Cross-Stitch Networks for Multi-task Learning (CVPR,2016) [[paper](https://arxiv.org/abs/1604.03539)] [[code](https://github.com/helloyide/Cross-stitch-Networks-for-Multi-task-Learning)]\n\n* Asymmetric Multi-task Learning based on Task Relatedness and Confidence (ICML, 2016) [[paper](http://proceedings.mlr.press/v48/leeb16.pdf)]\n\n* MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving (arXiv, 2016) [[paper](http://arxiv.org/abs/1612.07695)] [[code](https://github.com/MarvinTeichmann/MultiNet)]\n\n* A Unified Perspective on Multi-Domain and Multi-Task Learning (ICLR, 2015) [[paper](http://arxiv.org/abs/1412.7489)]\n\n* Facial Landmark Detection by Deep Multi-task Learning (ECCV, 2014) [[paper](https://personal.ie.cuhk.edu.hk/~ccloy/files/eccv_2014_deepfacealign.pdf)] [[code](http://mmlab.ie.cuhk.edu.hk/projects/TCDCN.html)]\n\n* Learning Task Grouping and Overlap in Multi-task Learning (ICML, 2012) [[paper](http://arxiv.org/abs/1206.6417)]\n\n* Learning with Whom to Share in Multi-task Feature Learning (ICML, 2011) [[paper](http://www.cs.utexas.edu/~grauman/papers/icml2011.pdf)]\n\n* Semi-Supervised Multi-Task Learning with Task Regularizations (ICDM, 2009) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=5360282)]\n\n* Semi-Supervised Multitask Learning (NeurIPS, 2008) [[paper](https://proceedings.neurips.cc/paper/2007/file/a34bacf839b923770b2c360eefa26748-Paper.pdf)]\n\n* Multitask Learning (1997) [[paper](https://link.springer.com/content/pdf/10.1023/A:1007379606734.pdf)]\n\n## [Awesome Multi-domain Multi-task Learning](https://github.com/WeiHongLee/Awesome-Multi-Domain-Multi-Task-Learning)\n\n## Workshops\n\n* [Universal Representations for Computer Vision Workshop at BMVC 2022](https://sites.google.com/view/universalrepresentations)\n\n* [Workshop on Multi-Task Learning in Computer Vision (DeepMTL) at ICCV 2021](https://sites.google.com/view/deepmtlworkshop/home)\n\n* [Adaptive and Multitask Learning: Algorithms \u0026 Systems Workshop (AMTL) at ICML 2019](https://www.amtl-workshop.org)\n\n* [Workshop on Multi-Task and Lifelong Reinforcement Learning at ICML 2015](https://sites.google.com/view/mtlrl)\n\n* [Transfer and Multi-Task Learning: Trends and New Perspectives at NeurIPS 2015](https://nips.cc/Conferences/2015/Schedule?showEvent=4939)\n\n* [Second Workshop on Transfer and Multi-task Learning at NeurIPS 2014](https://sites.google.com/site/multitaskwsnips2014/)\n\n* [New Directions in Transfer and Multi-Task: Learning Across Domains and Tasks Workshop at NeurIPS 2013](https://sites.google.com/site/learningacross/home)\n\n## Online Courses\n\n* [CS 330: Deep Multi-Task and Meta Learning](https://cs330.stanford.edu)\n\n## Related awesome list\n\n* https://github.com/SimonVandenhende/Awesome-Multi-Task-Learning\n\n* https://github.com/Manchery/awesome-multi-task-learning\n\n* https://github.com/junfish/Awesome-Multitask-Learning\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FWeiHongLee%2FAwesome-Multi-Task-Learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FWeiHongLee%2FAwesome-Multi-Task-Learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FWeiHongLee%2FAwesome-Multi-Task-Learning/lists"}