NC-TTT: A Noise Contrastive Approach for Test-Time Training
<|reference_start|>NC-TTT: A Noise Contrastive Approach for Test-Time Training: 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<|reference_end|>
@article{osowiechi2024nc-ttt:,
    title={NC-TTT: A Noise Contrastive Approach for Test-Time Training},
    author={David Osowiechi and Gustavo A. Vargas Hakim and Mehrdad Noori and
  Milad Cheraghalikhani and Ali Bahri and Moslem Yazdanpanah and Ismail Ben
  Ayed and Christian Desrosiers},
    journal={arXiv preprint arXiv:2404.08392},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08392},
    primaryClass={cs.CV cs.LG}
}