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"Effective robustness" measures the extra out-of-distribution (OOD) robustness beyond what can be predicted from the in-distribution (ID) performance.
Rank correlation methods
Kendall, M · 1948
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L., Li, K., and Li, F · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Yfcc100m: The new data in multimedia research
Thomee, B., Shamma, D. A., Friedland, G., Elizalde, B., Ni, K., Poland, D., Borth, D., and Li, L.-J · 2016
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Cinic-10 is not imagenet or cifar-10
Darlow, L. N., Crowley, E. J., Antoniou, A., and Storkey, A. J · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2018
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Do cifar-10 classifiers generalize to cifar-10?
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 2018
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Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Barbu, A., Mayo, D., Alverio, J., Luo, W., Wang, C., Gutfreund, D., Tenenbaum, J., and Katz, B · 2019
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Do imagenet classifiers generalize to imagenet?
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 2019
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Learning robust global representations by penalizing local predictive power
Wang, H., Ge, S., Lipton, Z., and Xing, E. P · 2019
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Cold case: The lost mnist digits
Yadav, C. and Bottou, L · 2019
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Harder or different? a closer look at distribution shift in dataset reproduction
Lu, S., Nott, B., Olson, A., Todeschini, A., Vahabi, H., Carmon, Y., and Schmidt, L · 2020
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The effect of natural distribution shift on question answering models
Miller, J., Krauth, K., Recht, B., and Schmidt, L · 2020
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Learning visual representations with caption annotations
Sariyildiz, M. B., Perez, J., and Larlus, D · 2020
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Measuring robustness to natural distribution shifts in image classification
Taori, R., Dave, A., Shankar, V., Carlini, N., Recht, B., and Schmidt, L · 2020
Cited alongside, same era.
Virtex: Learning visual representations from textual annotations
Desai, K. and Johnson, J · 2021
Cited alongside, same era.
Does language help generalization in vision models?
Devillers, B., Choksi, B., Bielawski, R., and VanRullen, R · 2021
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2021
Cited alongside, same era.
Openclip, 2021
Ilharco, G., Wortsman, M., Wightman, R., Gordon, C., Carlini, N., Taori, R., Dave, A., Shankar, V., Namkoong, H., Miller, J., Hajishirzi, H., Farhadi, A., and Schmidt, L · 2021
Cited alongside, same era.
Scaling up visual and vision-language representation learning with noisy text supervision
Agreement-on-the-line: Predicting the performance of neural networks under distribution shift
Baek, C., Jiang, Y., Raghunathan, A., and Kolter, J. Z · 2022
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Data determines distributional robustness in contrastive language image pre-training (clip)
Fang, A., Ilharco, G., Wortsman, M., Wan, Y., Shankar, V., Dave, A., and Schmidt, L · 2022
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Vision models are more robust and fair when pretrained on uncurated images without supervision
Goyal, P., Duval, Q., Seessel, I., Caron, M., Singh, M., Misra, I., Sagun, L., Joulin, A., and Bojanowski, P · 2022
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Imagenet-x: Understanding model mistakes with factor of variation annotations
Idrissi, B. Y., Bouchacourt, D., Balestriero, R., Evtimov, I., Hazirbas, C., Ballas, N., Vincent, P., Drozdzal, M., Lopez-Paz, D., and Ibrahim, M · 2022
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Hard imagenet: Segmentations for objects with strong spurious cues
Moayeri, M., Singla, S., and Feizi, S · 2022
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Jia, C., Yang, Y., Xia, Y., Chen, Y.-T., Parekh, Z., Pham, H., Le, Q., Sung, Y.-H., Li, Z., and Duerig, T · 2021
Cited alongside, same era.
Fine-tuning can distort pretrained features and underperform out-of-distribution
Kumar, A., Raghunathan, A., Jones, R. M., Ma, T., and Liang, P · 2021
Cited alongside, same era.
Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization
Miller, J. P., Taori, R., Raghunathan, A., Sagawa, S., Koh, P. W., Shankar, V., Liang, P., Carmon, Y., and Schmidt, L · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
Cited alongside, same era.
If your data distribution shifts, use self-learning
Rusak, E., Schneider, S., Pachitariu, G., Eck, L., Gehler, P. V., Bringmann, O., Brendel, W., and Bethge, M · 2021
Cited alongside, same era.
Laion-400m: Open dataset of clip-filtered 400 million image-text pairs
Schuhmann, C., Vencu, R., Beaumont, R., Kaczmarczyk, R., Mullis, C., Katta, A., Coombes, T., Jitsev, J., and Komatsuzaki, A · 2021
Cited alongside, same era.
Salient imagenet: How to discover spurious features in deep learning?
Singla, S. and Feizi, S · 2021
Cited alongside, same era.
Later among the works it cites.
Slip: Self-supervision meets language-image pre-training
Mu, N., Kirillov, A., Wagner, D., and Xie, S · 2022
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Quality not quantity: On the interaction between dataset design and robustness of clip
Nguyen, T., Ilharco, G., Wortsman, M., Oh, S., and Schmidt, L · 2022
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Is a caption worth a thousand images? a controlled study for representation learning
Santurkar, S., Dubois, Y., Taori, R., Liang, P., and Hashimoto, T · 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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Core risk minimization using salient imagenet
Singla, S., Moayeri, M., and Feizi, S · 2022
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Id and ood performance are sometimes inversely correlated on real-world datasets
Teney, D., Oh, S. J., and Abbasnejad, E · 2022
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When does dough become a bagel? analyzing the remaining mistakes on imagenet
Vasudevan, V., Caine, B., Gontijo Lopes, R., Fridovich-Keil, S., and Roelofs, R · 2022
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Contrastive learning of medical visual representations from paired images and text
Zhang, Y., Jiang, H., Miura, Y., Manning, C. D., and Langlotz, C. P · 2022
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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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