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awesome_OpenSetRecognition_list
A curated list of papers & resources linked to open set recognition, out-of-distribution, open set domain adaptation and open world recognition
https://github.com/iCGY96/awesome_OpenSetRecognition_list
- Toward Open Set Recognition
- Towards Open World Recognition
- Lifelong Machine Learning
- Recent Advances in Open Set Recognition: A Survey
- Recent Advances in Open Set Recognition: A Survey v2 - jun Huang, Songcan Chen. (**arXiv, 2019**).
- A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges
- Visual Perception and Learning in an Open World
- Dealing with Novelty in Open Worlds: DNOW
- Open World Image Classification Challenge
- Understanding open-set recognition by Jacobian norm and inter-class separation
- Deep Learning-Based Material Characterization Using FMCW Radar With Open-Set Recognition Technique - Abouzaid/MaterialCharML.git).
- From anomaly detection to open set recognition: Bridging the gap
- Large-Scale Open-Set Classification Protocols for ImageNet - IfI/openset-imagenet)
- The Devil is in the Wrongly-classified Samples: Towards Unified Open-set Recognition
- Class-specific semantic reconstruction for open set recognition - Ming Cheng. IEEE Transactions on Pattern Analysis and Machine Intelligence (**TPAMI 2022**). [[code]](https://github.com/xyzedd/CSSR).
- OpenAUC: Towards AUC-Oriented Open-Set Recognition
- Towards Open Set 3D Learning: A Benchmark on Object Point Clouds
- Measuring Human Perception to Improve Handwritten Document Transcription
- Open-Set Semi-Supervised Object Detection - Cheng Liu, Chih-Yao Ma, Xiaoliang Dai, Junjiao Tian, Peter Vajda, Zijian He, Zsolt Kira. (**ECCV 2022**). [[code]](https://github.com/facebookresearch/OSSOD).
- DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition
- Difficulty-Aware Simulator for Open Set Recognition - Ho Cho, Jae-Pil Heo. (**ECCV 2022**). [[code]](https://github.com/wjun0830/Difficulty-Aware-Simulator).
- Unseen Classes at a Later Time? No Problem - classes-at-a-later-time).
- OSSGAN: Open-Set Semi-Supervised Image Generation
- OpenTAL: Towards Open Set Temporal Action Localization
- The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods
- LUNA: Localizing Unfamiliarity Near Acquaintance for Open-Set Long-Tailed Recognition - Min Hsu, Jenq-Neng Hwang, Kelsey Magrane, Craig Rose. (**AAAI 2022**).
- Learngene: From Open-World to Your Learning Task
- Learning Network Architecture for Open-Set Recognition
- PMAL: Open Set Recognition via Robust Prototype Mining
- Open-Set Recognition: A Good Closed-Set Classifier is All You Need
- Adversarial Motorial Prototype Framework for Open Set Recognition
- OpenGAN: Open-Set Recognition via Open Data Generation
- Trash To Treasure: Harvesting OOD Data With Cross-Modal Matching for Open-Set Semi-Supervised Learning
- Energy-Based Open-World Uncertainty Modeling for Confidence Calibration
- Prototypical Matching and Open Set Rejection for Zero-Shot Semantic Segmentation
- Towards Discovery and Attribution of Open-World GAN Generated Images
- Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentation
- Deep Metric Learning for Open World Semantic Segmentation
- NGC: A Unified Framework for Learning With Open-World Noisy Data - Fan Wu, Tong Wei, Jianwen Jiang, Chaojie Mao, Mingqian Tang, Yu-Feng Li. (**ICCV 2021**)
- Large Scale Open-Set Deep Logo Detection - Yu Wu, Tian Cao, Bhargava Kota, Mehmet Tek. (**ArXiv 2021**). [[code]](https://github.com/mubastan/osld).
- OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers
- Zero-Shot Open Set Detection by Extending CLIP
- Adversarial Reciprocal Points Learning for Open Set Recognition
- Conditional Variational Capsule Network for Open Set Recognition
- Evidential Deep Learning for Open Set Action Recognition
- M2IOSR: Maximal Mutual Information Open Set Recognition - Voon Ling. (**ArXiv 2021**)
- OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers
- Exemplar-Based Open-Set Panoptic Segmentation Network - Young Lee, Bohyung Han. (**CVPR 2021**)
- Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios
- Open-set Face Recognition for Small Galleries Using Siamese Networks
- MMF: A loss extension for feature learning in open set recognition
- Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition
- Counterfactual Zero-Shot and Open-Set Visual Recognition - Sheng Hua. (**CVPR 2021**). [[code]](https://github.com/yue-zhongqi/gcm-cf).
- Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition
- Learning Placeholders for Open-Set Recognition - Wei Zhou, Han-Jia Ye, De-Chuan Zhan.(**CVPR 2021**)
- Teacher-Explorer-Student Learning: A Novel Learning Method for Open Set Recognition
- Conditional Gaussian Distribution Learning for Open Set Recognition - Voon Ling. (**CVPR 2020**). [[code]](https://github.com/BraveGump/CGDL-for-Open-Set-Recognition).
- Generative-discriminative Feature Representations for Open-set Recognition
- Few-Shot Open-Set Recognition Using Meta-Learning
- OpenGAN: Open Set Generative Adversarial Networks
- Collective decision for open set recognition
- One-vs-Rest Network-based Deep Probability Model for Open Set Recognition
- Hybrid Models for Open Set Recognition
- Learning Open Set Network with Discriminative Reciprocal Points
- Open-set Adversarial Defense
- Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning
- Class Anchor Clustering: a Distance-based Loss for Training Open Set Classifiers
- MMF: A loss extension for feature learning in open set recognition
- Fully Convolutional Open Set Segmentation
- S2OSC: A Holistic Semi-Supervised Approach for Open Set Classification - Qiang Sun, Hui Xiong, Jian Yang. (**ArXiv 2020**)
- Open Set Recognition with Conditional Probabilistic Generative Models - Voon Ling. (**ArXiv 2020**)
- Convolutional Prototype Network for Open Set Recognition - Ming Yang, Xu-Yao Zhang, Fei Yin, Qing Yang, Cheng-Lin Liu. (**PAMI 2020**)
- The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning
- Deep CNN-based Multi-task Learning for Open-Set Recognition
- Classification-Reconstruction Learning for Open-Set Recognition
- Alignment Based Matching Networks for One-Shot Classification and Open-Set Recognition
- Open-Set Recognition Using Intra-Class Splitting
- Experiments on Open-Set Speaker Identification with Discriminatively Trained Neural Networks
- Large-Scale Long-Tailed Recognition in an Open World - OLTR)
- Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?
- Deep Transfer Learning for Multiple Class Novelty Detection
- From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer - hust-2018-2011/S-DCNet)
- Open-set human activity recognition based on micro-Doppler signatures
- C2AE: Class Conditioned Auto-Encoder for Open-set Recognition
- Open category detection with PAC guarantees
- Open Set Text Classification using Convolutional Neural Networks
- Learning a Neural-network-based Representation for Open Set Recognition
- Unseen Class Discovery in Open-world Classification
- Reducing Network Agnostophobia - Network-Agnostophobia).
- Open Set Adversarial Examples
- Open Set Learning with Counterfactual Images - open-set)
- The extreme value machine
- Extreme Value Theory for Open Set Classification-GPD and GEV Classifiers
- Weightless neural networks for open set recognition
- Adversarial Robustness: Softmax versus Openmax
- DOC: Deep open classification of text documents - rakhlin/CNN-for-Sentence-Classification-in-Keras).
- Generative openmax for multi-class open set classification
- Open-category classification by adversarial sample generation
- Towards open set deep networks
- A bounded neural network for open set recognition
- Specialized Support Vector Machines for Open-set Recognition
- Data-Fusion Techniques for Open-Set Recognition Problems
- Towards open-set face recognition using hashing functions - HFCN-openset).
- Learning to Separate Domains in Generalized Zero-Shot and Open Set Learning: a probabilistic perspective - Gang Jiang, Xiangyang Xue. (**arXiv, 2018**).
- Sparse representation-based open set recognition
- Best fitting hyperplanes for classification
- Polyhedral conic classifiers for visual object detection and classification
- Fast and Accurate Face Recognition with Image Sets
- Nearest neighbors distance ratio open-set classifier
- Breaking the closed world assumption in text classification
- Probability models for open set recognition - openset).
- Multi-class open set recognition using probability of inclusion - openset).
- Toward Open Set Recognition - openset).
- Understanding open-set recognition by Jacobian norm and inter-class separation
- Learning Bounds for Open-Set Learning - Liu/Openset_Learning_AOSR).
- Learning Representations that Support Robust Transfer of Predictors
- Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain
- Entropy Maximization and Meta Classification for Out-of-Distribution Detection in Semantic Segmentation - chan/meta-ood)
- Online Continual Learning With Natural Distribution Shifts: An Empirical Study With Visual Data
- MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction - Taixe. (**ICCV 2021**). [**[code]**](https://github.com/selflein/MG-GAN)
- Semantically Coherent Out-of-Distribution Detection
- CODEs: Chamfer Out-of-Distribution Examples against Overconfidence Issue
- NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization - H. Gary Chan, Zhenguo Li. (**ICCV 2021**)
- CrossNorm and SelfNorm for Generalization under Distribution Shifts - research/crossnorm-selfnorm)
- Towards a Theoretical Framework of Out-of-Distribution Generalization
- Provably Robust Detection of Out-of-distribution Data (almost) for free
- Fine-grained Out-of-Distribution Detection with Mixup Outlier Exposure
- Multi-task Transformation Learning for Robust Out-of-Distribution Detection
- OoD-Bench: Benchmarking and Understanding Out-of-Distribution Generalization Datasets and Algorithms
- Exploring the Limits of Out-of-Distribution Detection
- Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?
- Out-of-Distribution Generalization in Kernel Regression
- MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space - wisc/large_scale_ood)
- MOOD: Multi-level Out-of-distribution Detection - wisc/MOOD)
- SSD: A Unified Framework for Self-Supervised Outlier Detection - group/SSD)
- Background Data Resampling for Outlier-Aware Classification - resample-ood)
- Robust Out-of-distribution Detection for Neural Networks - ood-detection)
- Input Complexity and Out-of-distribution Detection with Likelihood-based Generative Models
- Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data - Chang Hsu, Yilin Shen, Hongxia Jin, Zsolt Kira. (**CVPR 2020**)
- Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks
- The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization - r)
- The Effect of Optimization Methods on the Robustness of Out-of-Distribution Detection Approaches
- Density of States Estimation for Out-of-Distribution Detection
- Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks
- BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty - Velez, Weiwei Pan. (**ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning**)
- Contrastive Training for Improved Out-of-Distribution Detection
- OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification
- CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
- Iterative VAE as a predictive brain model for out-of-distribution generalization
- Certifiably Adversarially Robust Detection of Out-of-Distribution Data
- Deep Evidential Regression - deep-learning)
- Feature Space Singularity for Out-of-Distribution Detection - research/FSSD_OoD_Detection)
- Deep Anomaly Detection with Outlier Exposure - exposure)
- Do Deep Generative Models Know What They Don't Know?
- Likelihood Ratios for Out-of-Distribution Detection - research/google-research/tree/master/genomics_ood)
- Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy
- Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem - andr/relu_networks_overconfident)
- Outlier Exposure with Confidence Control for Out-of-Distribution Detection - Angelos Papadopoulos, Mohammad Reza Rajati, Nazim Shaikh, Jiamian Wang. (**arXiv, 2019**). [**[code]**](https://github.com/nazim1021/OOD-detection-using-OECC)
- Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty - ood)
- Evidential Deep Learning to Quantify Classification Uncertainty - classification-uncertainty)
- Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
- Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
- WAIC, but Why? Generative Ensembles for Robust Anomaly Detection
- A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
- Predictive uncertainty estimation via prior networks
- A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks - detection).
- need to survey more..
- Multiresolution Knowledge Distillation for Anomaly Detection - lab/Knowledge_Distillation_AD)
- Classification-Based Anomaly Detection for General Data
- A Benchmark for Anomaly Segmentation - seg).
- Deep Anomaly Detection Using Geometric Transformations - Yaniv. (**NeurIPS, 2018**). [**[code]**](https://github.com/izikgo/AnomalyDetectionTransformations).
- Towards Novel Target Discovery Through Open-Set Domain Adaptation
- Collaborative Training of Balanced Random Forests for Open Set Domain Adaptation
- Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation - Zhe Song, Jun Guo. (**arXiv 2020**). [**[code]**](https://github.com/dongliangchang/Mutual-to-Separate/)
- Towards Inheritable Models for Open-Set Domain Adaptation
- Exploring Category-Agnostic Clusters for Open-Set Domain Adaptation - Wah Ngo, Tao Mei. (**CVPR 2020**)
- On the Effectiveness of Image Rotation for Open Set Domain Adaptation
- Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation
- Progressive Graph Learning for Open-Set Domain Adaptation
- Adversarial Network with Multiple Classifiers for Open Set Domain Adaptation
- Separate to Adapt: Open Set Domain Adaptation via Progressive Separation
- Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization
- Weakly Supervised Open-Set Domain Adaptation by Dual-Domain Collaboration - Shi Zheng. (**CVPR 2019**). [**[code]**](https://github.com/junbaoZHUO/UODTN)
- Learning Factorized Representations for Open-set Domain Adaptation
- Known-class Aware Self-ensemble for Open Set Domain Adaptation
- Open Set Domain Adaptation: Theoretical Bound and Algorithm
- Open Set Domain Adaptation for Image and Action Recognition
- Attract or Distract: Exploit the Margin of Open Set
- Open set domain adaptation by backpropagation
- Open Set Domain Adaptation
- Label Efficient Learning of Transferable Representations across Domains and Tasks - Fei. (**NeurIPS 2017**).
- Opening Up Open-World Tracking - Taixé. (**CVPR 2022**). [**[code]**](https://github.com/JonathonLuiten/TrackEval/blob/master/docs/OpenWorldTracking-Official/Readme.md)
- OW-DETR: Open-world Detection Transformer - DETR)
- A Unified Objective for Novel Class Discovery
- Neighborhood Contrastive Learning for Novel Class Discovery
- OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in An Open World
- Towards Open World Object Detection
- Learning to discover novel visual categories via deep transfer clustering
- Automatically discovering and learning new visual categories with ranking statistics
- P-ODN: Prototype based Open Deep Network for Open Set Recognition
- Learning and the Unknown: Surveying Steps Toward Open World Recognition
- Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition
- Open-world Learning and Application to Product Classification - Open-World-Learning)
- Unseen Class Discovery in Open-world Classification
- The extreme value machine
- Learning to Accept New Classes without Training
- ODN: Opening the Deep Network for Open-Set Action Recognition
- Open-World Visual Recognition Using Knowledge Graphs
- Learning Cumulatively to Become More Knowledgeable
- Online open world recognition
- Towards Open World Recognition
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