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Going deeper with convolutions, 2014
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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J. L. Ba, J. R. Kiros, and G. E. Hinton · 2016
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Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
D. Hendrycks and K. Gimpel · 2017
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Densely connected convolutional networks
G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger · 2017
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Wide residual networks, 2017
S. Zagoruyko and N. Komodakis · 2017
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Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2018
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Progressive neural architecture search
C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L. Li, L. Fei-Fei, A. L. Yuille, J. Huang, and K. Murphy · 2018
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Minimal-entropy correlation alignment for unsupervised deep domain adaptation
P. Morerio, J. Cavazza, and V. Murino · 2018
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Do cifar-10 classifiers generalize to cifar-10?
B. Recht, R. Roelofs, L. Schimidt, and V. Shankar · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen · 2018
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Deep layer aggregation
F. Yu, D. Wang, E. Shelhamer, and T. Darrell · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
D. Hendrycks and T. Dietterich · 2019
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Do ImageNet classifiers generalize to ImageNet?
B. Recht, R. Roelofs, L. Schmidt, and V. Shankar · 2019
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Invariant risk minimization, 2020
M. Arjovsky, L. Bottou, I. Gulrajani, and D. Lopez-Paz · 2020
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Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
J. Liang, D. Hu, and J. Feng · 2020
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Designing network design spaces
I. Radosavovic, R. P. Kosaraju, R. Girshick, K. He, and P. Dollar · 2020
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Improving robustness against common corruptions by covariate shift adaptation
S. Schneider, E. Rusak, L. Eck, O. Bringmann, W. Brendel, and M. Bethge · 2020
Cited alongside, same era.
Test-time training with self-supervision for generalization under distribution shifts
Y. Sun, X. Wang, Z. Liu, J. Miller, A. A. Efros, and M. Hardt · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby · 2021
Cited alongside, same era.
Sharpness-aware minimization for efficiently improving generalization
P. Foret, A. Kleiner, H. Mobahi, and B. Neyshabur · 2021
Cited alongside, same era.
Leveraging unlabeled data to predict out-of-distribution performance
S. Garg, S. Balakrishnan, Z. C. Lipton, B. Neyshabur, and H. Sedghi · 2021
Cited alongside, same era.
Assessing generalization of SGD via disagreement
Y. Jiang, V. Nagarajan, C. Baek, and J. Z. Kolter · 2022
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Sita: Single image test-time adaptation, 2022
A. Khurana, S. Paul, P. Rai, S. Biswas, and G. Aggarwal · 2022
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Polyloss: A polynomial expansion perspective of classification loss functions
Z. Leng, M. Tan, C. Liu, E. D. Cubuk, J. Shi, S. Cheng, and D. Anguelov · 2022
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Efficient test-time model adaptation without forgetting
S. Niu, J. Wu, Y. Zhang, Y. Chen, S. Zheng, P. Zhao, and M. Tan · 2022
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If your data distribution shifts, use self-learning
E. Rusak, S. Schneider, G. Pachitariu, L. Eck, P. Gehler, O. Bringmann, W. Brendel, and M. Bethge · 2022
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Extending the wilds benchmark for unsupervised adaptation
S. Sagawa, P. W. Koh, T. Lee, I. Gao, S. M. Xie, K. Shen, A. Kumar, W. Hu, M. Yasunaga, H. Marklund, S. Beery, E. David, I. Stavness, W. Guo, J. Leskovec, K. Saenko, T. Hashimoto, S. Levine, C. Finn, and P. Liang · 2022
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D. Guillory, V. Shankar, S. Ebrahimi, T. Darrell, and L. Schmidt · 2021
Cited alongside, same era.
In search of lost domain generalization
I. Gulrajani and D. Lopez-Paz · 2021
Cited alongside, same era.
The many faces of robustness: A critical analysis of out-of-distribution generalization
D. Hendrycks, S. Basart, N. Mu, S. Kadavath, F. Wang, E. Dorundo, R. Desai, T. Zhu, S. Parajuli, M. Guo, D. Song, J. Steinhardt, and J. Gilmer · 2021
Cited alongside, same era.
Test-time classifier adjustment module for model-agnostic domain generalization
Y. Iwasawa and Y. Matsuo · 2021
Cited alongside, same era.
Why do classifier accuracies show linear trends under distribution shift?, 2021
H. Mania and S. Sra · 2021
Cited alongside, same era.
Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization
J. P. Miller, R. Taori, A. Raghunathan, S. Sagawa, P. W. Koh, V. Shankar, P. Liang, Y. Carmon, and L. Schmidt · 2021
Cited alongside, same era.
Tune it the right way: Unsupervised validation of domain adaptation via soft neighborhood density
K. Saito, D. Kim, P. Teterwak, S. Sclaroff, T. Darrell, and K. Saenko · 2021
Cited alongside, same era.
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Deit iii: Revenge of the vit
H. Touvron, M. Cord, and H. J’egou · 2022
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Continual test-time domain adaptation
Q. Wang, O. Fink, L. Van Gool, and D. Dai · 2022
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Assaying out-of-distribution generalization in transfer learning
F. Wenzel, A. Dittadi, P. V. Gehler, C.-J. Simon-Gabriel, M. Horn, D. Zietlow, D. Kernert, C. Russell, T. Brox, B. Schiele, B. Schölkopf, and F. Locatello · 2022
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MEMO: Test time robustness via adaptation and augmentation
M. Zhang, S. Levine, and C. Finn · 2022
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Mixed samples as probes for unsupervised model selection in domain adaptation
D. Hu, J. Liang, J. H. Liew, C. Xue, S. Bai, and X. Wang · 2023
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Demystifying disagreement-on-the-line in high dimensions
D. Lee, B. Moniri, X. Huang, E. Dobriban, and H. Hassani · 2023
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Three new validators and a large-scale benchmark ranking for unsupervised domain adaptation, 2023
K. Musgrave, S. Belongie, and S.-N. Lim · 2023
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Towards stable test-time adaptation in dynamic wild world
S. Niu, J. Wu, Y. Zhang, Z. Wen, Y. Chen, P. Zhao, and M. Tan · 2023
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Id and ood performance are sometimes inversely correlated on real-world datasets, 2023
D. Teney, Y. Lin, S. J. Oh, and E. Abbasnejad · 2023
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Assessing model out-of-distribution generalization with softmax prediction probability baselines and a correlation method, 2023
W. Tu, W. Deng, T. Gedeon, and L. Zheng · 2023
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Towards understanding gd with hard and conjugate pseudo-labels for test-time adaptation
J.-K. Wang and A. Wibisono · 2023
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On pitfalls of test-time adaptation
H. Zhao, Y. Liu, A. Alahi, and T. Lin · 2023
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Monotonic risk relationships under distribution shifts for regularized risk minimization
D. LeJeune, J. Liu, and R. Heckel · 2024
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