Test-time adaptation (TTA) enhances the zero-shot robustness under distribution shifts by leveraging unlabeled test data during inference. Despite notable advances, several challenges still limit its broader applicability. First, most methods rely on backpropagation or iterative optimization, which limits scalability and hinders real-time deployment.  Second, they lack explicit modeling of class-conditional feature distributions. This modeling is crucial for producing reliable decision boundaries and calibrated predictions, but it remains underexplored due to the lack of both source data and supervision at test time. In this paper, we propose an Advanced Distribution-Aware and backPropagation-free Test-time adaptation (ADAPT) method. We reframe TTA as a Gaussian probabilistic inference task by modeling class-conditional likelihoods using gradually updated class means and a shared covariance matrix. This enables closed-form, training-free inference. To correct potential likelihood bias, we introduce lightweight regularization guided by CLIP priors and a historical knowledge bank. ADAPT requires no source data, no gradient updates, and no full access to target data, supporting both online and transductive settings. Extensive experiments across diverse benchmarks demonstrate that our method achieves state-of-the-art performance under a wide range of distribution shifts with superior scalability and robustness. 

Python >= 3.10  
PyTorch == 2.5.1

bash ADAPT_online_TTA.sh

bash ADAPT_Transductive_TTA.sh

@inproceedings{zhangbackpropagation,
  title={Backpropagation-Free Test-Time Adaptation via Probabilistic Gaussian Alignment},
  author={Zhang, Youjia and Kim, Youngeun and Choi, Young-Geun and Kim, Hongyeob and Liu, Huiling and Hong, Sungeun},
  booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems}
}