DeepLearning
A collection of research papers, datasets and software on Deep Learning
https://github.com/axruff/DeepLearning
Last synced: 5 days ago
JSON representation
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Models
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- 2014 - **[Seq2Seq
- 2012 - **[AlexNet
- 2014 - **[OverFeat
- 1998 - **[LeNet
- 2013 - Learning Hierarchical Features for Scene Labeling
- 2013 - **[R-CNN
- 2014 - **[Seq2Seq
- 2014 - **[VGG
- 2014 - **[GoogleNet
- 2014 - Neural Turing Machines
- 2015 - **[ResNet
- 2015 - Spatial Transformer Networks
- 2016 - **[WRN - residual-networks)
- 2015 - **[FCN
- 2015 - **[U-net
- 2016 - **[Xception
- Implementation
- 2016 - **[V-Net
- 2017 - **[MobileNets
- Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
- 2018 - **[TernausNet
- 2018 - CubeNet: Equivariance to 3D Rotation and Translation
- 2018 - Deep Rotation Equivariant Network
- 2018 - ArcFace: Additive Angular Margin Loss for Deep Face Recognition
- 2019 - **[PacNet
- 2019 - Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation
- 2019 - Panoptic Feature Pyramid Networks
- 2019 - **[DeeperLab
- 2019 - **[EfficientNet
- 2019 - Hamiltonian Neural Networks
- 2020 - Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis
- 2020 - Neural Operator: Graph Kernel Network for Partial Differential Equations
- 2021 - Learning Neural Network Subspaces
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Composition
- 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
- 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
- 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
- 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
- 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
- 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
- 2019 - Local Relation Networks for Image Recognition
- 2017 - Teaching Compositionality to CNNs
- 2020 - Concept Bottleneck Models
- 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
- 2013 - Complexity of Representation and Inference in Compositional Models with Part Sharing
- 2017 - Interpretable Convolutional Neural Networks
- 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
- 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
- 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
- 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
- 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
- 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
- 2005 - Image Parsing: Unifying Segmentation, Detection, and Recognition
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Multi-level
- 2016 - **[DeepLab
- 2017 - Rethinking Atrous Convolution for Semantic Image Segmentation
- 2014 - **[SPP-Net
- 2016 - **[ParseNet
- 2016 - **[PSPNet
- 2015 - Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net
- 2016 - Attention to Scale: Scale-aware Semantic Image Segmentation
- 2017 - Feature Pyramid Networks for Object Detection
- 2018 - **[DeepLabv3
- 2019 - **[FastFCN
- 2019 - Making Convolutional Networks Shift-Invariant Again
- 2019 - **[LEDNet
- 2019 - Feature Pyramid Encoding Network for Real-time Semantic Segmentation
- 2019 - Efficient Segmentation: Learning Downsampling Near Semantic Boundaries
- 2019 - PointRend: Image Segmentation as Rendering
- 2019 - Fixing the train-test resolution discrepancy
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Context and Attention
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Capsule Networks
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Transformers
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3D Shape
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Logic and Semantics
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Unsupervised Learning
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Logic and Semantics
- 2020 - **::SURVEY::** Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey
- 2019 - Unsupervised Learning via Meta-Learning
- 2019 - **[PIRL
- 2019 - Representation Learning with Contrastive Predictive Coding
- 2019 - **[MoCo
- 2015 - Unsupervised Visual Representation Learning by Context Prediction
- 2016 - Colorful Image Colorization
- 2016 - Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
- 2016 - Context Encoders: Feature Learning by Inpainting
- 2018 - Unsupervised Representation Learning by Predicting Image Rotations
- 2019 - Greedy InfoMax for Biologically Plausible Self-Supervised Representation Learning
- 2020 - **[SimCLR
- 2020 - **[NeurIPS 2020 Workshop
- 2020 - **[BYOL
- 2021 - [POST
- 2021 - Task Fingerprinting for Meta Learning in Biomedical Image Analysis
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Reinforcement Learning
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Inverse Reinforcement Learning
- 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
- 2019 - Deep learning optoacoustic tomography with sparse data
- 2020 - **[Review
- 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
- 2020 - Automating turbulence modelling by multi-agent reinforcement learning
- 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
- 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
- GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks (2018)
- 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
- 2019 - Deep learning optoacoustic tomography with sparse data
- 2020 - **[Review
- 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
- 2020 - Automating turbulence modelling by multi-agent reinforcement learning
- 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
- 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
- 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
- 2019 - Deep learning optoacoustic tomography with sparse data
- 2020 - **[Review
- 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
- 2020 - Automating turbulence modelling by multi-agent reinforcement learning
- 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
- 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
- 2019 - Deep learning optoacoustic tomography with sparse data
- 2020 - **[Review
- 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
- 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
- 2020 - Automating turbulence modelling by multi-agent reinforcement learning
- 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
- 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
- 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
- 2019 - Deep learning optoacoustic tomography with sparse data
- 2020 - **[Review
- 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
- 2020 - Automating turbulence modelling by multi-agent reinforcement learning
- 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
- 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
- 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
- 2019 - Deep learning optoacoustic tomography with sparse data
- 2020 - **[Review
- 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
- 2020 - Automating turbulence modelling by multi-agent reinforcement learning
- 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
- 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
- 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
- 2019 - Deep learning optoacoustic tomography with sparse data
- 2020 - **[Review
- 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
- 2020 - Automating turbulence modelling by multi-agent reinforcement learning
- 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
- 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
- 2019 - On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference
- **[OPENSURFACES
- **[ShapeNet
- ShapeNet: An Information-Rich 3D Model Repository
- [paper
- 2015 - Natural Language Object Retrieval
- 2019 - CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions
- **[3D ShapeNets
- **[BLEND SWAP
- **[DTD
- **[MegaDepth
- Microsoft **[COCO
- 2020 - **[CARLA
- A Browsable Petascale Reconstruction of the Human Cortex
- 2021 - Medical Segmentation Decathlon. Generalisable 3D Semantic Segmentation
- DAWNBench: is a benchmark suite for end-to-end deep learning training and inference.
- DAWNBench: An End-to-End Deep Learning Benchmark and Competition (paper) (2017)
- BubGAN: Bubble Generative Adversarial Networks for Synthesizing Realistic Bubbly Flow Images
- 2020 - **[Review
- 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
- 2020 - End-To-End Convolutional Neural Network for 3D Reconstruction of Knee Bones from Bi-planar X-Ray Images
- Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping (2018)
- Deep learning with domain adaptation for accelerated projection‐reconstruction MR (2017)
- Abdominal multi-organ segmentation with organ-attention networks and statistical fusion (2018)
- Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation (2019)
- Breast Tumor Segmentation and Shape Classification in Mammograms using Generative Adversarial and Convolutional Neural Network (2018)
- 2019 - H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
- 2020 - **[TorchIO
- 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
- 2019 - Deep learning optoacoustic tomography with sparse data
- 2019 - A deep learning reconstruction framework for X-ray computed tomography with incomplete data
- 2020 - Deep Learning Techniques for Inverse Problems in Imaging
- 2020 - Differentiated Backprojection Domain Deep Learning for Conebeam Artifact Removal
- 2020 - Extreme Sparse X-ray Computed Laminography Via Convolutional Neural Networks
- 2021 - **[SliceGAN
- 2021 - DeepPhase: Learning phase contrast signal from dual energy X-ray absorption images
- 2022 - Machine learning denoising of high-resolution X-ray nanotomography data
- 2014 - Do Convnets Learn Correspondence?
- 2016 - Universal Correspondence Network
- 2016 - Learning Dense Correspondence via 3D-guided Cycle Consistency
- 2017 - Convolutional neural network architecture for geometric matching - rocco/cnngeometric_pytorch)
- 2018 - **[DGC-Net - Net)
- 2018 - Residual Dense Network for Image Restoration
- 2018 - Image Super-Resolution Using Very Deep Residual Channel Attention Networks
- 2019 - Noise2Self: Blind Denoising by Self-Supervision
- 2018 - An Unsupervised Learning Model for Deformable Medical Image Registration
- 2018 - VoxelMorph: A Learning Framework for Deformable Medical Image Registration
- 2019 - A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration
- 2019 - Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
- 2020 - RANSAC-Flow: generic two-stage image alignment
- Video-to-Video Synthesis (2018)
- 2017 - PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume - pwc)
- 2020 - Softmax Splatting for Video Frame Interpolation - splatting)
- 2017 - **[TOFlow
- 2020 - Automating turbulence modelling by multi-agent reinforcement learning
- 2017 - "Zero-Shot" Super-Resolution using Deep Internal Learning
- 2020 - Improving Blind Spot Denoising for Microscopy
- 2021 - Denoising-based Image Compression for Connectomics
- 2018 - Image Inpainting for Irregular Holes Using Partial Convolutions - Keras)
- 2017 - Globally and Locally Consistent Image Completion
- 2017 - Generative Image Inpainting with Contextual Attention
- 2018 - Free-Form Image Inpainting with Gated Convolution
- Photo-realistic single image super-resolution using a generative adversarial network (2016)
- Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification (2018)
- 2020 - **[Review
- 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
- 2019 - Deep learning optoacoustic tomography with sparse data
- 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
- 2020 - Automating turbulence modelling by multi-agent reinforcement learning
- **[mlflow
- OpenAI Microscope
- A Closed-form Solution to Photorealistic Image Stylization (2018)
- 2021 - COIN: COmpression with Implicit Neural representations
- **[pix2code
- Fast Interactive Object Annotation with Curve-GCN (2019)
- 2017 - Learning Fashion Compatibility with Bidirectional LSTMs
- 2020 - A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
- 2020 - Fourier Neural Operator for Parametric Partial Differential Equations
- **Caffe**: Convolutional Architecture for Fast Feature Embedding
- **PySyft**: A generic framework for privacy preserving deep learning
- **Crypten**: A framework for Privacy Preserving Machine Learning
- **[Snorkel
- **[Rapid
- **[DALI
- **[PhotonAI
- **[DeepImageJ
- **[ImJoy
- **[BioImage.IO
- **[DeepImageTranslator
- **[OpenMMLab
- 2016 - An Analysis of Deep Neural Network Models for Practical Applications
- 2017 - Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
- 2019 - High-performance medicine: the convergence of human and artificial intelligence
- 2020 - Maithra Raghu, Eric Schmidt. A Survey of Deep Learning for Scientific Discovery
- 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
- 2016 - Building Machines That Learn and Think Like People
- 2016 - A Berkeley View of Systems Challenges for AI
- 2018 - Deep Learning: A Critical Appraisal
- 2018 - When Will AI Exceed Human Performance? Evidence from AI Experts
- 2018 - The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation
- 2018 - Deciphering China’s AI Dream: The context, components, capabilities, and consequences of China’s strategy to lead the world in AI
- 2018 - The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
- 2019 - Deep Nets: What have they ever done for Vision?
- 2020 - State of AI Report 2020
- 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
- 2020 - The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence
- 2021 - Why AI is Harder Than We Think by Melanie Mitchell
- 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
- 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
- MLPerf: A broad ML benchmark suite for measuring performance of ML software frameworks, ML hardware accelerators, and ML cloud platforms.
- 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
- 2019 - Deep learning optoacoustic tomography with sparse data
- 2020 - **[Review
- 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
- 2020 - Automating turbulence modelling by multi-agent reinforcement learning
- 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
- 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
- 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
- 2019 - Deep learning optoacoustic tomography with sparse data
- 2020 - **[Review
- 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
- 2020 - Automating turbulence modelling by multi-agent reinforcement learning
- 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
- 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
- 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
- 2019 - Deep learning optoacoustic tomography with sparse data
- 2020 - **[Review
- 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
- 2020 - Automating turbulence modelling by multi-agent reinforcement learning
- 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
- 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
- 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
- 2019 - Deep learning optoacoustic tomography with sparse data
- 2020 - **[Review
- 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
- 2020 - Automating turbulence modelling by multi-agent reinforcement learning
- 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
- 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
- 2020 - Reconstructing lost BOLD signal in individual participants using deep machine learning
- 2019 - Deep learning optoacoustic tomography with sparse data
- 2020 - **[Review
- 2020 - Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.
- 2020 - Automating turbulence modelling by multi-agent reinforcement learning
- **Tune**: A Research Platform for Distributed Model Selection and Training (2018) - project/ray/tree/master/python/ray/tune)
- 2021 - Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
- 2020 - The role of artificial intelligence in achieving the Sustainable Development Goals
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Optimization and Regularisation
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Logic and Semantics
- Random search for hyper-parameter optimisation
- **[Adam
- 2017 - The Marginal Value of Adaptive Gradient Methods in Machine Learning
- 2017 - Understanding deep learning requires rethinking generalization
- 2018 - Error Forward-Propagation: Reusing Feedforward Connections to Propagate Errors in Deep Learning
- 2018 - An Empirical Model of Large-Batch Training
- 2018 - A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay
- 2019 - Cyclical Learning Rates for Training Neural Networks
- 2019 - DeepOBS: A Deep Learning Optimizer Benchmark Suite
- 2019 - Switchable Normalization for Learning-to-Normalize Deep Representation
- 2019 - Revisiting Small Batch Training for Deep Neural Networks
- 2019 - A Recipe for Training Neural Networks. Andrey Karpathi Blog
- 2020 - Fantastic Generalization Measures and Where to Find Them
- 2020 - Descending through a Crowded Valley -- Benchmarking Deep Learning Optimizers
- 2020 - Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth
- 2021 - Revisiting ResNets: Improved Training and Scaling Strategies
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Pruning and Compression
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Logic and Semantics
- 2013 - Do Deep Nets Really Need to be Deep?
- 2018 - Rethinking the Value of Network Pruning
- 2018 - Slimmable Neural Networks
- 2019 - Universally Slimmable Networks and Improved Training Techniques
- 2015 - Learning both Weights and Connections for Efficient Neural Networks
- 2015 - Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
- 2015 - Distilling the Knowledge in a Neural Network
- 2017 - Learning Efficient Convolutional Networks through Network Slimming - [[github]](https://github.com/liuzhuang13/slimming) ⭕
- 2019 - The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
- 2019 - AutoSlim: Towards One-Shot Architecture Search for Channel Numbers
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Analysis and Interpretability
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Logic and Semantics
- 2015 - Visualizing and Understanding Recurrent Networks
- 2016 - Discovering Causal Signals in Images
- 2016 - **[Grad-CAM - grad-cam)
- 2017 - Visualizing the Loss Landscape of Neural Nets
- 2019 - **[SURVEY
- 2018 - GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
- 2018 Interactive tool
- 2020 - Shortcut Learning in Deep Neural Networks
- 2021 - VIDEO: CVPR 2021 Workshop. Interpretable Neural Networks for Computer Vision: Clinical Decisions that are Aided, not Automated
- 2021 - VIDEO. CVPR 2021 Workshop. Interpreting Deep Generative Models for Interactive AI Content Creation by Bolei Zhou (CUHK)
- 2019 - **[Distill
- 2019 - On the Units of GANs
- 2019 - Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
- 2020 - Actionable Attribution Maps for Scientific Machine Learning
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Segmentation
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Logic and Semantics
- 2019 - Panoptic Segmentation
- 2019 - The Best of Both Modes: Separately Leveraging RGB and Depth for Unseen Object Instance Segmentation
- 2019 - ShapeMask: Learning to Segment Novel Objects by Refining Shape Priors
- 2019 - Learning to Segment via Cut-and-Paste
- 2019 - YOLACT Real-time Instance Segmentation
- 2021 - Boundary IoU: Improving Object-Centric Image Segmentation Evaluation - iou/)
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Instance Segmentation
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Interactive Segmentation
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Anomaly Detection
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Semantic Correspondence
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Logic and Semantics
- 2017 - End-to-end weakly-supervised semantic alignment
- 2019 - SFNet: Learning Object-aware Semantic Correspondence - [[github]](https://github.com/cvlab-yonsei/SFNet)
- 2020 - Deep Semantic Matching with Foreground Detection and Cycle-Consistency
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Weakly Supervised
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Logic and Semantics
- 2015 - Constrained Convolutional Neural Networks for Weakly Supervised Segmentation
- 2020 - Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods
- 2018 - Deep Learning with Mixed Supervision for Brain Tumor Segmentation
- 2019 - Localization with Limited Annotation for Chest X-rays
- 2019 - Doubly Weak Supervision of Deep Learning Models for Head CT
- 2019 - Training Complex Models with Multi-Task Weak Supervision
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Optical Flow
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Logic and Semantics
- 2019 - SelFlow: Self-Supervised Learning of Optical Flow - [github]](https://github.com/ppliuboy/SelFlow)
- 2021 - AutoFlow: Learning a Better Training Set for Optical Flow
- 2021 - SMURF: Self-Teaching Multi-Frame Unsupervised RAFT with Full-Image Warping
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Semi Supervised
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Logic and Semantics
- 2017 - Random Erasing Data Augmentation - Erasing)
- 2017 - Smart Augmentation - Learning an Optimal Data Augmentation Strategy
- 2017 - Population Based Training of Neural Networks
- 2018 - **[Survey
- 2018 - Albumentations: fast and flexible image augmentations - [[github]](https://github.com/albu/albumentations) ✅
- 2018 - Data Augmentation by Pairing Samples for Images Classification
- 2018 - **[AutoAugment
- 2019 - **[UDA - [[github]](https://github.com/google-research/uda) ⭕
- 2019 - **[MixMatch
- 2019 - **[RealMix
- 2019 - Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
- 2019 - **[AugMix - research/augmix) ✅
- 2019 - Self-training with **[Noisy Student
- 2020 - Rain rendering for evaluating and improving robustness to bad weather
- 2014 - Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
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Mutual Learning
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Multitask Learning
Categories
Reinforcement Learning
196
Models
81
Unsupervised Learning
16
Optimization and Regularisation
16
Semi Supervised
15
Analysis and Interpretability
14
Pruning and Compression
10
Segmentation
6
Weakly Supervised
6
Multitask Learning
3
Optical Flow
3
Semantic Correspondence
3
Instance Segmentation
2
Mutual Learning
2
Anomaly Detection
2
Interactive Segmentation
1