Publications related to OptTTA: Learnable Test-Time Augmentation for Source-Free Medical Image Segmentation Under Domain Shift

Towards Robust Vision Transformer

Recent advances on Vision Transformer (ViT) and its improved variants have shown that self-attention-based networks surpass traditional Convolutional Neural Networks (CNNs) in most vision tasks. However, existing ViTs focus on the standard accuracy and com ...

IEEE COMPUTER SOC2022

Continual Test-Time Domain Adaptation

Olga Fink, Qin Wang

Test-time domain adaptation aims to adapt a source pretrained model to a target domain without using any source data. Existing works mainly consider the case where the target domain is static. However, real-world machine perception systems are running in n ...

IEEE COMPUTER SOC2022

Robustness and invariance properties of image classifiers

Apostolos Modas

Deep neural networks have achieved impressive results in many image classification tasks. However, since their performance is usually measured in controlled settings, it is important to ensure that their decisions remain correct when deployed in noisy envi ...

EPFL2022

Robust Generalization despite Distribution Shift via Minimum Discriminating Information

Daniel Kuhn, Andreas Krause, Tobias Sutter

Training models that perform well under distribution shifts is a central challenge in machine learning. In this paper, we introduce a modeling framework where, in addition to training data, we have partial structural knowledge of the shifted test distribut ...

2021

Disparity Between Batches as a Signal for Early Stopping

Patrick Thiran, Mahsa Forouzesh

We propose a metric for evaluating the generalization ability of deep neural networks trained with mini-batch gradient descent. Our metric, called gradient disparity, is the l2 norm distance between the gradient vectors of two mini-batches drawn from the t ...

Springer2021

Multiple testing with test statistics following heavy-tailed distributions

Zhiwen Jiang

In multiple testing problems where the components come from a mixture model of noise and true effect, we seek to first test for the existence of the non-zero components, and then identify the true alternatives under a fixed significance level

\alpha

. Two ...

EPFL2021

Redundant features can hurt robustness to distributions shift

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Guillermo Ortiz Jimenez, Apostolos Modas

In this work, we borrow tools from the field of adversarial robustness, and propose a new framework that permits to relate dataset features to the distance of samples to the decision boundary. Using this framework we identify the subspace of features used ...

2020

On Vacuous and Non-Vacuous Generalization Bounds for Deep Neural Networks.

Konstantinos Pitas

Deep neural networks have been empirically successful in a variety of tasks, however their theoretical understanding is still poor. In particular, modern deep neural networks have many more parameters than training data. Thus, in principle they should over ...

EPFL2020

Redundant features can hurt robustness to distribution shift

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Guillermo Ortiz Jimenez, Apostolos Modas

In this work, we borrow tools from the field of adversarial robustness, and propose a new framework that permits to relate dataset features to the distance of samples to the decision boundary. Using this framework we identify the subspace of features used ...

2020

RobustBench: a standardized adversarial robustness benchmark

Nicolas Henri Bernard Flammarion, Maksym Andriushchenko, Francesco Croce

Evaluation of adversarial robustness is often error-prone leading to overestimation of the true robustness of models. While adaptive attacks designed for a particular defense are a way out of this, there are only approximate guidelines on how to perform th ...

2020

OptTTA: Learnable Test-Time Augmentation for Source-Free Medical Image Segmentation Under Domain Shift

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Towards Robust Vision Transformer

Continual Test-Time Domain Adaptation

Robustness and invariance properties of image classifiers

Robust Generalization despite Distribution Shift via Minimum Discriminating Information

Disparity Between Batches as a Signal for Early Stopping

Multiple testing with test statistics following heavy-tailed distributions

Redundant features can hurt robustness to distributions shift

On Vacuous and Non-Vacuous Generalization Bounds for Deep Neural Networks.

Redundant features can hurt robustness to distribution shift

RobustBench: a standardized adversarial robustness benchmark

Robustness and invariance properties of image classifiers

Continual Test-Time Domain Adaptation

Towards Robust Vision Transformer

Robust Generalization despite Distribution Shift via Minimum Discriminating Information

Disparity Between Batches as a Signal for Early Stopping

Multiple testing with test statistics following heavy-tailed distributions

RobustBench: a standardized adversarial robustness benchmark

Redundant features can hurt robustness to distribution shift

Redundant features can hurt robustness to distributions shift

On Vacuous and Non-Vacuous Generalization Bounds for Deep Neural Networks.