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Pretrained machine learning models need to be adapted to distribution shifts when deployed in new target environments.
The componentwise distance to the nearest singular matrix
Demmel, J · 1992
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Dataset shift in machine learning
Quinonero-Candela, J., Sugiyama, M., Schwaighofer, A., and Lawrence, N. D · 2008
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Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias
Fang, C., Xu, Y., and Rockmore, D. N · 2013
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Learning and transferring mid-level image representations using convolutional neural networks
Oquab, M., Bottou, L., Laptev, I., and Sivic, J · 2014
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Cnn features off-the-shelf: an astounding baseline for recognition
Sharif Razavian, A., Azizpour, H., Sullivan, J., and Carlsson, S · 2014
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Deep domain confusion: Maximizing for domain invariance
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., and Darrell, T · 2014
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H · 2014
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Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., March, M., and Lempitsky, V · 2016
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Deeper, broader and artier domain generalization
Li, D., Yang, Y., Song, Y.-Z., and Hospedales, T. M · 2017
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Convolutional neural networks analyzed via convolutional sparse coding
Papyan, V., Romano, Y., and Elad, M · 2017
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Deep hashing network for unsupervised domain adaptation
Venkateswara, H., Eusebio, J., Chakraborty, S., and Panchanathan, S · 2017
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mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2017
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Recognition in terra incognita
Beery, S., Van Horn, G., and Perona, P · 2018
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On the importance of single directions for generalization
Morcos, A. S., Barrett, D. G., Rabinowitz, N. C., and Botvinick, M · 2018
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Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
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A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
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Do better imagenet models transfer better?
Kornblith, S., Shlens, J., and Le, Q. V · 2019
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Generalization guarantees for neural networks via harnessing the low-rank structure of the jacobian
Oymak, S., Fabian, Z., Li, M., and Soltanolkotabi, M · 2019
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Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 2019
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Manifold mixup: Better representations by interpolating hidden states
Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Lopez-Paz, D., and Bengio, Y · 2019
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High-dimensional statistics: A non-asymptotic viewpoint , volume 48
Wainwright, M. J · 2019
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A large-scale study of representation learning with the visual task adaptation benchmark
Zhai, X., Puigcerver, J., Kolesnikov, A., Ruyssen, P., Riquelme, C., Lucic, M., Djolonga, J., Pinto, A. S., Neumann, M., Dosovitskiy, A., et al · 2019
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Test-time training with masked autoencoders
Gandelsman, Y., Sun, Y., Chen, X., and Efros, A · 2022
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On feature learning in the presence of spurious correlations
Izmailov, P., Kirichenko, P., Gruver, N., and Wilson, A. G · 2022
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Last layer re-training is sufficient for robustness to spurious correlations
Kirichenko, P., Izmailov, P., and Wilson, A. G · 2022
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Fine-tuning can distort pretrained features and underperform out-of-distribution
Kumar, A., Raghunathan, A., Jones, R., Ma, T., and Liang, P · 2022
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You only need a good embeddings extractor to fix spurious correlations
Mehta, R., Albiero, V., Chen, L., Evtimov, I., Glaser, T., Li, Z., and Hassner, T · 2022
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Gulrajani, I. and Lopez-Paz, D · 2020
Cited alongside, same era.
Learning from failure: De-biasing classifier from biased classifier
Nam, J., Cha, H., Ahn, S., Lee, J., and Shin, J · 2020
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An investigation of why overparameterization exacerbates spurious correlations
Sagawa, S., Raghunathan, A., Koh, P. W., and Liang, P · 2020
Cited alongside, same era.
Test-time training with self-supervision for generalization under distribution shifts
Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A., and Hardt, M · 2020
Cited alongside, same era.
Test-time unsupervised domain adaptation
Varsavsky, T., Orbes-Arteaga, M., Sudre, C. H., Graham, M. S., Nachev, P., and Cardoso, M. J · 2020
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Tent: Fully test-time adaptation by entropy minimization
Wang, D., Shelhamer, E., Liu, S., Olshausen, B., and Darrell, T · 2020
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Linear unit-tests for invariance discovery
Aubin, B., Słowik, A., Arjovsky, M., Bottou, L., and Lopez-Paz, D · 2021
Cited alongside, same era.
Environment inference for invariant learning
Creager, E., Jacobsen, J.-H., and Zemel, R · 2021
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Agree to disagree: Diversity through disagreement for better transferability
Pagliardini, M., Jaggi, M., Fleuret, F., and Karimireddy, S. P · 2022
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Rosenfeld, E., Ravikumar, P., and Risteski, A · 2022
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Revisiting weakly supervised pre-training of visual perception models
Singh, M., Gustafson, L., Adcock, A., de Freitas Reis, V., Gedik, B., Kosaraju, R. P., Mahajan, D., Girshick, R., Dollár, P., and Van Der Maaten, L · 2022
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Evading the simplicity bias: Training a diverse set of models discovers solutions with superior ood generalization
Teney, D., Abbasnejad, E., Lucey, S., and Van den Hengel, A · 2022
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Robust fine-tuning of zero-shot models
Wortsman, M., Ilharco, G., Kim, J. W., Li, M., Kornblith, S., Roelofs, R., Lopes, R. G., Hajishirzi, H., Farhadi, A., Namkoong, H., et al · 2022
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Improving out-of-distribution robustness via selective augmentation
Yao, H., Wang, Y., Li, S., Zhang, L., Liang, W., Zou, J., and Finn, C · 2022
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Contrastive adapters for foundation model group robustness
Zhang, M. and Ré, C · 2022
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Few-shot adaptation of pre-trained networks for domain shift
Zhang, W., Shen, L., Zhang, W., and Foo, C.-S · 2022
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Progressive mix-up for few-shot supervised multi-source domain transfer
Zhu, R., Yu, X., and Li, S · 2022
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Project and probe: Sample-efficient domain adaptation by interpolating orthogonal features
Chen, A. S., Lee, Y., Setlur, A., Levine, S., and Finn, C · 2023
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A whac-a-mole dilemma: Shortcuts come in multiples where mitigating one amplifies others
Li, Z., Evtimov, I., Gordo, A., Hazirbas, C., Hassner, T., Ferrer, C. C., Xu, C., and Ibrahim, M · 2023
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Simple and fast group robustness by automatic feature reweighting
Qiu, S., Potapczynski, A., Izmailov, P., and Wilson, A. G · 2023
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Understanding the robustness of multi-modal contrastive learning to distribution shift
Xue, Y., Joshi, S., Nguyen, D., and Mirzasoleiman, B · 2023
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Change is hard: A closer look at subpopulation shift
Yang, Y., Zhang, H., Katabi, D., and Ghassemi, M · 2023
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