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

Gabriele Valvano, Andrea Leo, Sotirios A. Tsaftaris

Research output: Working paperPreprint

Abstract / Description of output

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.
Original languageEnglish
PublisherArXiv
DOIs
Publication statusPublished - 2021

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  • Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training

    Valvano, G., Leo, A. & Tsaftaris, S. A., 21 Sept 2021, Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health: Third MICCAI Workshop, DART 2021, and First MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, Proceedings. Albarqouni, S., Cardoso, M. J., Dou, Q., Kamnitsas, K., Khanal, B., Rekik, I., Rieke, N., Sheet, D., Tsaftaris, S., Xu, D. & Xu, Z. (eds.). Springer, p. 68-78 11 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12968 LNCS).

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