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02/20/2023
Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts
Adversarial training is widely used to make classifiers robust to a spec...
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10/10/2022
Revisiting adapters with adversarial training
While adversarial training is generally used as a defense mechanism, rec...
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11/09/2021
Data Augmentation Can Improve Robustness
Adversarial training suffers from robust overfitting, a phenomenon where...
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10/21/2021
A Fine-Grained Analysis on Distribution Shift
Robustness to distribution shifts is critical for deploying machine lear...
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10/18/2021
Improving Robustness using Generated Data
Recent work argues that robust training requires substantially larger da...
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06/29/2021
AutoNovel: Automatically Discovering and Learning Novel Visual Categories
We tackle the problem of discovering novel classes in an image collectio...
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04/02/2021
Defending Against Image Corruptions Through Adversarial Augmentations
Modern neural networks excel at image classification, yet they remain vu...
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03/02/2021
Fixing Data Augmentation to Improve Adversarial Robustness
Adversarial training suffers from robust overfitting, a phenomenon where...
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06/17/2020
LSD-C: Linearly Separable Deep Clusters
We present LSD-C, a novel method to identify clusters in an unlabeled da...
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04/06/2020
There and Back Again: Revisiting Backpropagation Saliency Methods
Saliency methods seek to explain the predictions of a model by producing...
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02/13/2020
Automatically Discovering and Learning New Visual Categories with Ranking Statistics
We tackle the problem of discovering novel classes in an image collectio...
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10/19/2019
NormGrad: Finding the Pixels that Matter for Training
The different families of saliency methods, either based on contrastive ...
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05/21/2019
Semi-Supervised Learning with Scarce Annotations
While semi-supervised learning (SSL) algorithms provide an efficient way...
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03/27/2018
Efficient parametrization of multi-domain deep neural networks
A practical limitation of deep neural networks is their high degree of s...
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05/22/2017
Learning multiple visual domains with residual adapters
There is a growing interest in learning data representations that work w...
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11/23/2016