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

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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

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

Graph Chatbot

Chattez avec Graph Search

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

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

Towards Robust Vision Transformer

Continual Test-Time Domain Adaptation

Disparity Between Batches as a Signal for Early Stopping

Redundant features can hurt robustness to distributions shift

Robustness and invariance properties of image classifiers

Robust Generalization despite Distribution Shift via Minimum Discriminating Information

Multiple testing with test statistics following heavy-tailed distributions

RobustBench: a standardized adversarial robustness benchmark

Redundant features can hurt robustness to distribution shift