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An evaluation criterion for safe and trustworthy deep learning is how well the invariances captured by representations of deep neural networks (DNNs) are shared with humans.
Adversarial training for free!
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Adversarial examples are not bugs, they are features
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Zoom In: An Introduction to Circuits
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Do adversarially robust imagenet models transfer better?
Salman, H.; Ilyas, A.; Engstrom, L.; Kapoor, A.; and Madry, A. 2020 · 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 · 2020
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Grounding representation similarity with statistical testing
Ding, F.; Denain, J.-S.; and Steinhardt, J. 2021 · 2021
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Enriching ImageNet With Human Similarity Judgments and Psychological Embeddings
Roads, B. D.; and Love, B. C. 2021 · 2021
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Measuring Representational Robustness of Neural Networks Through Shared Invariances
Nanda, V.; Speicher, T.; Kolling, C.; Dickerson, J. P.; Gummadi, K.; and Weller, A. 2022 · 2022
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