A curated list of papers & resources linked to open set recognition, out-of-distribution, open set domain adaptation and open world recognition
A curated list of papers & ressources linked to open set recognition, out-of-distribution, open set domain adaptation, and open world recognition
Note that:
Toward Open Set Recognition, Scheirer W J, de Rezende Rocha A, Sapkota A, et al. (PAMI, 2013).
Towards Open World Recognition, Bendale A, Boult T. (CVPR, 2015).
Lifelong Machine Learning, Zhiyuan Chen and Bing Liu. (2018).
Recent Advances in Open Set Recognition: A Survey, Geng C, Huang S, Chen S. (arXiv, 2018).
Recent Advances in Open Set Recognition: A Survey v2, Chuanxing Geng, Sheng-jun Huang, Songcan Chen. (arXiv, 2019).
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges. Salehi M, Mirzaei H, Hendrycks D, Li Y, Rohban MH, Sabokrou M. (arXiv 2021).
Learning Representations that Support Robust Transfer of Predictors. Yilun Xu, Tommi Jaakkola. (ArXiv 2021). [code]
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain. Guangyao Chen, Peixi Peng, Li Ma, Jia Li, Lin Du, Yonghong Tian. (ICCV 2021). [code]
Entropy Maximization and Meta Classification for Out-of-Distribution Detection in Semantic Segmentation. Robin Chan, Matthias Rottmann, Hanno Gottschalk. (ICCV 2021). [code]
Online Continual Learning With Natural Distribution Shifts: An Empirical Study With Visual Data. Zhipeng Cai, Ozan Sener, Vladlen Koltun. (ICCV 2021). [code]
MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction. Patrick Dendorfer, Sven Elflein, Laura Leal-Taixe. (ICCV 2021). [code]
Semantically Coherent Out-of-Distribution Detection. Jingkang Yang, Haoqi Wang, Litong Feng, Xiaopeng Yan, Huabin Zheng, Wayne Zhang, Ziwei Liu. (ICCV 2021). [code]
CODEs: Chamfer Out-of-Distribution Examples against Overconfidence Issue. Keke Tang, Dingruibo Miao, Weilong Peng, Jianpeng Wu, Yawen Shi, Zhaoquan Gu, Zhihong Tian, Wenping Wang. (ICCV 2021)
NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization. Haoyue Bai, Fengwei Zhou, Lanqing Hong, Nanyang Ye, S.-H. Gary Chan, Zhenguo Li. (ICCV 2021)
CrossNorm and SelfNorm for Generalization under Distribution Shifts. Zhiqiang Tang, Yunhe Gao, Yi Zhu, Zhi Zhang, Mu Li, Dimitris Metaxas. (ICCV 2021). [code]
Towards a Theoretical Framework of Out-of-Distribution Generalization. Haotian Ye, Chuanlong Xie, Tianle Cai, Ruichen Li, Zhenguo Li, Liwei Wang. (ArXiv 2021).
Provably Robust Detection of Out-of-distribution Data (almost) for free. Alexander Meinke, Julian Bitterwolf, Matthias Hein. (ArXiv 2021).
Fine-grained Out-of-Distribution Detection with Mixup Outlier Exposure. Jingyang Zhang, Nathan Inkawhich, Yiran Chen, Hai Li. (ArXiv 2021).
Multi-task Transformation Learning for Robust Out-of-Distribution Detection. Sina Mohseni, Arash Vahdat, Jay Yadawa. (ArXiv 2021).
OoD-Bench: Benchmarking and Understanding Out-of-Distribution Generalization Datasets and Algorithms. Nanyang Ye, Kaican Li, Lanqing Hong, Haoyue Bai, Yiting Chen, Fengwei Zhou, Zhenguo Li. (ArXiv 2021).
Exploring the Limits of Out-of-Distribution Detection. Stanislav Fort, Jie Ren, Balaji Lakshminarayanan. (ArXiv 2021).
Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?. Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron Courville. (ICML 2021).
Out-of-Distribution Generalization in Kernel Regression. Abdulkadir Canatar, Blake Bordelon, Cengiz Pehlevan. (ArXiv 2021).
MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space. Rui Huang, Yixuan Li. (CVPR 2021). [code]
MOOD: Multi-level Out-of-distribution Detection. Ziqian Lin, Sreya Dutta Roy, Yixuan Li. (CVPR 2021). [code]
SSD: A Unified Framework for Self-Supervised Outlier Detection, Vikash Sehwag, Mung Chiang, Prateek Mittal. (ICLR 2021). [code]
A Unified Objective for Novel Class Discovery, Enrico Fini, Enver Sangineto, Stéphane Lathuilière, Zhun Zhong, Moin Nabi, Elisa Ricci. (ICCV 2021). [code]
Neighborhood Contrastive Learning for Novel Class Discovery, Zhun Zhong, Enrico Fini, Subhankar Roy, Zhiming Luo, Elisa Ricci, Nicu Sebe. (CVPR 2021). [code]
OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in An Open World, Zhun Zhong, Linchao Zhu, Zhiming Luo, Shaozi Li, Yi Yang, Nicu Sebe. (CVPR 2021).
Towards Open World Object Detection, K J Joseph, Salman Khan, Fahad Shahbaz Khan, Vineeth N Balasubramanian. (CVPR 2021). [code]
Learning to discover novel visual categories via deep transfer clustering, Kai Han, Andrea Vedaldi, Andrew Zisserman. (ICCV 2019).
Automatically discovering and learning new visual categories with ranking statistics, Han, K., Rebuffi, S.A., Ehrhardt, S., Vedaldi, A., Zisserman. (ICLR 2020).
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