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Robustmix: Improving Robustness by Regularizing the Frequency Bias of Deep Nets

Published in NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and Applications, 2022, 2022

An extension of mixup in the frequency domain that regularizes the deep nets for robustness to common corruptions.

Recommended citation: Ngnawe, J., NJIFON, M. A., Heek, J., and Dauphin, Y. Robustmix: Improving robustness by regularizing the frequency bias of deep nets. In NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and Applications, 2022. URL https://openreview.net/forum?id=Na64z0YpOx.
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Layerwise Early Stopping for Test Time Adaptation.

Published in Arxiv, 2024

A gradient alignement method for layer selection in Test Time Adaptation.

Recommended citation: Sahoo, S., ElAraby, M., Ngnawe, J., Pequignot, Y., Precioso, F., & Gagné, C. (2024). Layerwise Early Stopping for Test Time Adaptation. arXiv preprint arXiv:2404.03784.
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Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers.

Published in Neurips 2024, 2024

A novel property of deep robust classifiers that allows to use the logit margin as a proxy score for input margin and efficiently detect non-robust samples, vulnerable to adversarial attacks.

Recommended citation: Ngnawé, J., Sahoo, S., Pequignot, Y., Precioso, F., & Gagné, C. (2024). Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers. arXiv preprint arXiv:2406.18451.
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