Memo: Test time robustness via adaptation and augmentation

M Zhang, S Levine, C Finn - Advances in neural information …, 2022 - proceedings.neurips.cc
While deep neural networks can attain good accuracy on in-distribution test points, many
applications require robustness even in the face of unexpected perturbations in the input …
被引用次数：204 相关文章

MEMO: Test Time Robustness via Adaptation and Augmentation

MM Zhang, S Levine, C Finn - Advances in Neural Information …, 2022 - openreview.net
While deep neural networks can attain good accuracy on in-distribution test points, many
applications require robustness even in the face of unexpected perturbations in the input …

MEMO: Test Time Robustness via Adaptation and Augmentation

M Zhang, S Levine, C Finn - arXiv e-prints, 2021 - ui.adsabs.harvard.edu
While deep neural networks can attain good accuracy on in-distribution test points, many
applications require robustness even in the face of unexpected perturbations in the input …

MEMO: test time robustness via adaptation and augmentation

M Zhang, S Levine, C Finn - … of the 36th International Conference on …, 2022 - dl.acm.org
While deep neural networks can attain good accuracy on in-distribution test points, many
applications require robustness even in the face of unexpected perturbations in the input …

MEMO: Test Time Robustness via Adaptation and Augmentation

M Zhang, S Levine, C Finn - arXiv preprint arXiv:2110.09506, 2021 - arxiv.org
While deep neural networks can attain good accuracy on in-distribution test points, many
applications require robustness even in the face of unexpected perturbations in the input …

MEMO: Test Time Robustness via Adaptation and Augmentation

MM Zhang, S Levine, C Finn - NeurIPS 2021 Workshop on Distribution … - openreview.net
While deep neural networks can attain good accuracy on in-distribution test points, many
applications require robustness even in the face of unexpected perturbations in the input …