Re-using Adversarial Mask Discriminators for Test-time Training under Distribution Shifts

TL;DR

This paper proposes re-using trained adversarial discriminators from GANs during inference to detect and correct segmentation errors under distribution shifts, enhancing test-time performance in medical imaging.

Contribution

It introduces methods to make discriminators reusable at inference, combining them with image reconstruction costs for improved test-time training, and explores their potential for continual learning.

Findings

Re-using discriminators improves segmentation accuracy during inference.

Combining discriminators with reconstruction costs enhances test-time training.

Method is compatible with post-processing and online learning.

Abstract

Thanks to their ability to learn flexible data-driven losses, Generative Adversarial Networks (GANs) are an integral part of many semi- and weakly-supervised methods for medical image segmentation. GANs jointly optimise a generator and an adversarial discriminator on a set of training data. After training is complete, the discriminator is usually discarded, and only the generator is used for inference. But should we discard discriminators? In this work, we argue that training stable discriminators produces expressive loss functions that we can re-use at inference to detect and \textit{correct} segmentation mistakes. First, we identify key challenges and suggest possible solutions to make discriminators re-usable at inference. Then, we show that we can combine discriminators with image reconstruction costs (via decoders) to endow a causal perspective to test-time training and further improve the model. Our method is simple and improves the test-time performance of pre-trained GANs. Moreover, we show that it is compatible with standard post-processing techniques and it has the potential to be used for Online Continual Learning. With our work, we open new research avenues for re-using adversarial discriminators at inference. Our code is available at https://vios-s.github.io/adversarial-test-time-training.