  Despite their exceptional performance in vision tasks, deep learning models
often struggle when faced with domain shifts during testing. Test-Time Training
(TTT) methods have recently gained popularity by their ability to enhance the
robustness of models through the addition of an auxiliary objective that is
jointly optimized with the main task. Being strictly unsupervised, this
auxiliary objective is used at test time to adapt the model without any access
to labels. In this work, we propose Noise-Contrastive Test-Time Training
(NC-TTT), a novel unsupervised TTT technique based on the discrimination of
noisy feature maps. By learning to classify noisy views of projected feature
maps, and then adapting the model accordingly on new domains, classification
performance can be recovered by an important margin. Experiments on several
popular test-time adaptation baselines demonstrate the advantages of our method
compared to recent approaches for this task. The code can be found
at:https://github.com/GustavoVargasHakim/NCTTT.git
