Overcoming Distribution Shifts in Plug-and-Play Methods with Test-Time Training
<|reference_start|>Overcoming Distribution Shifts in Plug-and-Play Methods with Test-Time Training: Plug-and-Play Priors (PnP) is a well-known class of methods for solving inverse problems in computational imaging. PnP methods combine physical forward models with learned prior models specified as image denoisers. A common issue with the learned models is that of a performance drop when there is a distribution shift between the training and testing data. Test-time training (TTT) was recently proposed as a general strategy for improving the performance of learned models when training and testing data come from different distributions. In this paper, we propose PnP-TTT as a new method for overcoming distribution shifts in PnP. PnP-TTT uses deep equilibrium learning (DEQ) for optimizing a self-supervised loss at the fixed points of PnP iterations. PnP-TTT can be directly applied on a single test sample to improve the generalization of PnP. We show through simulations that given a sufficient number of measurements, PnP-TTT enables the use of image priors trained on natural images for image reconstruction in magnetic resonance imaging (MRI).<|reference_end|>
@article{chandler2024overcoming,
    title={Overcoming Distribution Shifts in Plug-and-Play Methods with Test-Time
  Training},
    author={Edward P. Chandler, Shirin Shoushtari, Jiaming Liu, M. Salman Asif,
  and Ulugbek S. Kamilov},
    journal={2023 IEEE 9th International Workshop on Computational Advances in
  Multi-Sensor Adaptive Processing (CAMSAP), 2023, pg. 186-190},
    year={2024},
    doi={10.1109/CAMSAP58249.2023.10403502},
    archivePrefix={arXiv},
    eprint={2403.10374},
    primaryClass={eess.IV cs.CV}
}