PROGRAM: PROtotype GRAph Model based Pseudo-Label Learning for Test-Time Adaptation

Haopeng Sun, Lumin Xu, Sheng Jin, Ping Luo, Chen Qian, Wentao Liu

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Keywords: test-time adaptation, domain adaptation, domain shift

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TL;DR: This work proposes a novel test-time adaptation method, namely PROGRAM, for robust target domain adaptation by exploiting noisy pseudo-labels.

Abstract: Test-time adaptation (TTA) aims to adapt a pre-trained model from a source domain to a target domain only using online unlabeled target data during testing, without accessing to the source data or modifying the original training process. Among the various TTA methods, pseudo-labeling has gained popularity. However, the presence of incorrect pseudo-labels can hinder the effectiveness of target domain adaptation. To overcome this challenge, we propose a novel TTA method, called PROtotype GRAph Model based pseudo-label learning (PROGRAM). PROGRAM consists of two key components: (1) Prototype Graph Model (PGM) for reliable pseudo-label generation; (2) Robust Self-Training (RST) for test-time adaptation with noisy pseudo-labels. PGM constructs the graph using prototypes and test samples, facilitating effective message passing among them to generate more reliable pseudo-labels. RST combines the advantages of consistency regularization and pseudo-labeling to achieve robust target domain adaptation in the presence of noisy pseudo-labels. Our proposed PROGRAM can be easily integrated into existing baselines, resulting in consistent improvement. Extensive experiments show that our PROGRAM outperforms the existing TTA methods on multiple domain generalization and image corruption benchmarks.

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Primary Area: transfer learning, meta learning, and lifelong learning

Submission Number: 7713