Fetching the paper…
Reading the bibliography…
Out-of-distribution (OOD) detection aims to detect test samples that do not fall into any training in-distribution (ID) classes.
P. J. Huber, “Robust estimation of a location parameter,” in Breakthroughs in statistics , 1992
1992
Earlier work this paper cites.
X. Li and D. Roth, “Experimental data for question classification,” Cognitive Computation Group, Department of Computer Science, University of Illinois at Urbana-Champaign,(URL: http://l2r. cs. uiuc. edu/% 7Ecogcomp/Data/QA/QC/) , 2002
2002
Earlier work this paper cites.
A. Krizhevsky, G. Hinton et al. , “Learning multiple layers of features from tiny images,” Tech Report , 2009
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in CVPR , 2009
2009
Earlier work this paper cites.
2010
Earlier work this paper cites.
J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba, “Sun database: Large-scale scene recognition from abbey to zoo,” in CVPR , 2010
2010
Earlier work this paper cites.
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, “Reading digits in natural images with unsupervised feature learning,” in NeurIPS , 2011
2011
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg et al. , “Scikit-learn: Machine learning in python,” JMLR , 2011
2011
Earlier work this paper cites.
A. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, “Learning word vectors for sentiment analysis,” in ACL , 2011
2011
Earlier work this paper cites.
J. Hoffman, T. Darrell, and K. Saenko, “Continuous manifold based adaptation for evolving visual domains,” in CVPR , 2014
2014
Earlier work this paper cites.
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” NeurIPS , 2014
2014
Earlier work this paper cites.
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi, “Describing textures in the wild,” in CVPR , 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature , 2015
2015
Earlier work this paper cites.
A. Nguyen, J. Yosinski, and J. Clune, “Deep neural networks are easily fooled: High confidence predictions for unrecognizable images,” in CVPR , 2015
2015
Earlier work this paper cites.
A. Bendale and T. Boult, “Towards open world recognition,” in CVPR , 2015
2015
Earlier work this paper cites.
A. Royer and C. H. Lampert, “Classifier adaptation at prediction time,” in CVPR , 2015
2015
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in ICML , 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
S. R. Bowman, G. Angeli, C. Potts, and C. D. Manning, “A large annotated corpus for learning natural language inference,” in EMNLP , 2015
2015
Earlier work this paper cites.
A. Bendale and T. E. Boult, “Towards open set deep networks,” in CVPR , 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
D. Elliott, S. Frank, K. Sima’an, and L. Specia, “Multi30k: Multilingual english-german image descriptions,” in Proceedings of the 5th Workshop on Vision and Language , 2016, pp. 70–74
2016
Earlier work this paper cites.
O. Bojar, R. Chatterjee, C. Federmann, Y. Graham, B. Haddow, M. Huck, A. J. Yepes, P. Koehn, V. Logacheva, C. Monz et al. , “Findings of the 2016 conference on machine translation (wmt16),” in First conference on machine translation , 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
S. Merity, C. Xiong, J. Bradbury, and R. Socher, “Pointer sentinel mixture models,” in ICLR , 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in ECCV , 2016
2016
Earlier work this paper cites.
S. Zagoruyko and N. Komodakis, “Wide residual networks,” in BMVC , 2016
2016
Earlier work this paper cites.
S. Caelles, K.-K. Maninis, J. Pont-Tuset, L. Leal-Taixé, D. Cremers, and L. Van Gool, “One-shot video object segmentation,” in CVPR , 2017
2017
Earlier work this paper cites.
B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba, “Places: A 10 million image database for scene recognition,” IEEE TPAMI , 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
S. Liang, Y. Li, and R. Srikant, “Enhancing the reliability of out-of-distribution image detection in neural networks,” in ICLR , 2018
2018
Earlier work this paper cites.
K. Lee, K. Lee, H. Lee, and J. Shin, “A simple unified framework for detecting out-of-distribution samples and adversarial attacks,” in NeurIPS , 2018
2018
Earlier work this paper cites.
S. Gidaris, P. Singh, and N. Komodakis, “Unsupervised representation learning by predicting image rotations,” ICLR , 2018
2018
Earlier work this paper cites.
A. Bobu, E. Tzeng, J. Hoffman, and T. Darrell, “Adapting to continuously shifting domains,” 2018
2018
Earlier work this paper cites.
S. Gidaris and N. Komodakis, “Dynamic few-shot visual learning without forgetting,” in CVPR , 2018
2018
Earlier work this paper cites.
G. Van Horn, O. Mac Aodha, Y. Song, Y. Cui, C. Sun, A. Shepard, H. Adam, P. Perona, and S. Belongie, “The inaturalist species classification and detection dataset,” in CVPR , 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
D. Hendrycks, M. Mazeika, and T. Dietterich, “Deep anomaly detection with outlier exposure,” ICLR , 2019
2019
Earlier work this paper cites.
A. Meinke and M. Hein, “Towards neural networks that provably know when they don’t know,” in ICLR , 2019
2019
Earlier work this paper cites.
G. I. Parisi, R. Kemker, J. L. Part, C. Kanan, and S. Wermter, “Continual lifelong learning with neural networks: A review,” Neural networks , 2019
2019
Cited alongside, same era.
C. Finn, A. Rajeswaran, S. Kakade, and S. Levine, “Online meta-learning,” in ICML , 2019
2019
Cited alongside, same era.
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” in ICML , 2019
2019
Cited alongside, same era.
H. Cai, C. Gan, T. Wang, Z. Zhang, and S. Han, “Once-for-all: Train one network and specialize it for efficient deployment,” in ICLR , 2019
2019
Cited alongside, same era.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al. , “Pytorch: An imperative style, high-performance deep learning library,” in NeurIPS , 2019
A. G. Roy, J. Ren, S. Azizi, A. Loh, V. Natarajan, B. Mustafa, N. Pawlowski, J. Freyberg, Y. Liu, Z. Beaver et al. , “Does your dermatology classifier know what it doesn’t know? detecting the long-tail of unseen conditions,” Medical Image Analysis , 2022
2022
Later among the works it cites.
J. Katz-Samuels, J. Nakhleh, R. Nowak, and Y. Li, “Training ood detectors in their natural habitats,” in ICML , 2022
2022
Later among the works it cites.
D. Hendrycks, S. Basart, M. Mazeika, A. Zou, J. Kwon, M. Mostajabi, J. Steinhardt, and D. Song, “Scaling out-of-distribution detection for real-world settings,” in ICML , 2022
2022
Later among the works it cites.
H. Wei, R. Xie, H. Cheng, L. Feng, B. An, and Y. Li, “Mitigating neural network overconfidence with logit normalization,” in ICML , 2022
2022
Later among the works it cites.
Y. Ming, Y. Fan, and Y. Li, “Poem: Out-of-distribution detection with posterior sampling,” in ICML , 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
R. Wightman, “Pytorch image models,” https://github.com/rwightman/pytorch-image-models , 2019
2019
Cited alongside, same era.
K. He, R. Girshick, and P. Dollár, “Rethinking imagenet pre-training,” in ICCV , 2019
2019
Cited alongside, same era.
A. Filos, P. Tigkas, R. McAllister, N. Rhinehart, S. Levine, and Y. Gal, “Can autonomous vehicles identify, recover from, and adapt to distribution shifts?” in ICML , 2020
2020
Cited alongside, same era.
W. Liu, X. Wang, J. Owens, and Y. Li, “Energy-based out-of-distribution detection,” in NeurIPS , 2020
2020
Cited alongside, same era.
V. Sehwag, M. Chiang, and P. Mittal, “Ssd: A unified framework for self-supervised outlier detection,” in ICLR , 2020
2020
Cited alongside, same era.
S. Mohseni, M. Pitale, J. Yadawa, and Z. Wang, “Self-supervised learning for generalizable out-of-distribution detection,” in AAAI , 2020
2020
Cited alongside, same era.
J. Liang, D. Hu, and J. Feng, “Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation,” in ICML , 2020
2020
Cited alongside, same era.
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Sun, Y. Ming, X. Zhu, and Y. Li, “Out-of-distribution detection with deep nearest neighbors,” in ICML , 2022
2022
Later among the works it cites.
P. Morteza and Y. Li, “Provable guarantees for understanding out-of-distribution detection,” in AAAI , 2022
2022
Later among the works it cites.
Y. Sun and Y. Li, “Dice: Leveraging sparsification for out-of-distribution detection,” in ECCV , 2022
2022
Later among the works it cites.
X. Du, Z. Wang, M. Cai, and Y. Li, “Vos: Learning what you don’t know by virtual outlier synthesis,” in ICLR , 2022
2022
Later among the works it cites.
Y. Liu, Y. Chen, W. Dai, M. Gou, C.-T. Huang, and H. Xiong, “Source-free domain adaptation with contrastive domain alignment and self-supervised exploration for face anti-spoofing,” in ECCV , 2022
2022
Later among the works it cites.
J. N. Kundu, S. Bhambri, A. Kulkarni, H. Sarkar, V. Jampani, and R. V. Babu, “Concurrent subsidiary supervision for unsupervised source-free domain adaptation,” in ECCV , 2022
2022
Later among the works it cites.
Y. Gandelsman, Y. Sun, X. Chen, and A. Efros, “Test-time training with masked autoencoders,” NeurIPS , 2022
2022
Later among the works it cites.
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” in CVPR , 2022
2022
Later among the works it cites.
M. Jang, S.-Y. Chung, and H. W. Chung, “Test-time adaptation via self-training with nearest neighbor information,” ICLR , 2022
2022
Later among the works it cites.
S. Niu, J. Wu, Y. Zhang, Y. Chen, S. Zheng, P. Zhao, and M. Tan, “Efficient test-time model adaptation without forgetting,” in ICML , 2022
2022
Later among the works it cites.
S. Goyal, M. Sun, A. Raghunathan, and J. Z. Kolter, “Test time adaptation via conjugate pseudo-labels,” NeurIPS , 2022
2022
Later among the works it cites.
Q. Wang, O. Fink, L. Van Gool, and D. Dai, “Continual test-time domain adaptation,” in CVPR , 2022
2022
Later among the works it cites.
M. Boudiaf, R. Mueller, I. Ben Ayed, and L. Bertinetto, “Parameter-free online test-time adaptation,” in CVPR , 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
W. Fedus, B. Zoph, and N. Shazeer, “Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,” JMLR , 2022
2022
Later among the works it cites.
E. B. Zaken, Y. Goldberg, and S. Ravfogel, “Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models,” in ACL , 2022
2022
Later among the works it cites.
M. Jia, L. Tang, B.-C. Chen, C. Cardie, S. Belongie, B. Hariharan, and S.-N. Lim, “Visual prompt tuning,” in ECCV , 2022
2022
Later among the works it cites.
Y.-L. Sung, J. Cho, and M. Bansal, “Lst: Ladder side-tuning for parameter and memory efficient transfer learning,” NeurIPS , 2022
2022
Later among the works it cites.
D. Lian, D. Zhou, J. Feng, and X. Wang, “Scaling & shifting your features: A new baseline for efficient model tuning,” NeurIPS , 2022
2022
Later among the works it cites.
H. Wang, Z. Li, L. Feng, and W. Zhang, “Vim: Out-of-distribution with virtual-logit matching,” in CVPR , 2022
2022
Later among the works it cites.
Y. Ming, Y. Sun, O. Dia, and Y. Li, “How to exploit hyperspherical embeddings for out-of-distribution detection?” in ICLR , 2023
2023
Closest in time.
X. Du, Y. Sun, X. Zhu, and Y. Li, “Dream the impossible: Outlier imagination with diffusion models,” in NeurIPS , 2023
2023
Closest in time.
Z. Zhang and X. Xiang, “Decoupling maxlogit for out-of-distribution detection,” in CVPR , 2023
2023
Closest in time.
X. Liu, Y. Lochman, and C. Zach, “Gen: Pushing the limits of softmax-based out-of-distribution detection,” in CVPR , 2023
2023
Closest in time.
Y. Yu, S. Shin, S. Lee, C. Jun, and K. Lee, “Block selection method for using feature norm in out-of-distribution detection,” in CVPR , 2023
2023
Closest in time.
Q. Wang, J. Ye, F. Liu, Q. Dai, M. Kalander, T. Liu, J. HAO, and B. Han, “Out-of-distribution detection with implicit outlier transformation,” in ICLR , 2023
2023
Closest in time.
L. Tao, X. Du, X. Zhu, and Y. Li, “Non-parametric outlier synthesis,” in ICLR , 2023
2023
Closest in time.
2023
Closest in time.
D. Osowiechi, G. A. V. Hakim, M. Noori, M. Cheraghalikhani, I. Ben Ayed, and C. Desrosiers, “Tttflow: Unsupervised test-time training with normalizing flow,” in WACV , 2023
2023
Closest in time.
S. Niu, J. Wu, Y. Zhang, Z. Wen, Y. Chen, P. Zhao, and M. Tan, “Towards stable test-time adaptation in dynamic wild world,” ICLR , 2023
2023
Closest in time.
J. Song, J. Lee, I. S. Kweon, and S. Choi, “Ecotta: Memory-efficient continual test-time adaptation via self-distilled regularization,” in CVPR , 2023
2023
Closest in time.
M. Döbler, R. A. Marsden, and B. Yang, “Robust mean teacher for continual and gradual test-time adaptation,” in CVPR , 2023
2023
Closest in time.
Y. Gan, Y. Bai, Y. Lou, X. Ma, R. Zhang, N. Shi, and L. Luo, “Decorate the newcomers: Visual domain prompt for continual test time adaptation,” in AAAI , 2023
2023
Closest in time.
C.-H. Tu, Z. Mai, and W.-L. Chao, “Visual query tuning: Towards effective usage of intermediate representations for parameter and memory efficient transfer learning,” in CVPR , 2023
2023
Closest in time.