Context-Aware Self-Adaptation for Domain Generalization

• URL: http://arxiv.org/abs/2504.03064v1
• Date: Thu, 03 Apr 2025 22:33:38 GMT
• Title: Context-Aware Self-Adaptation for Domain Generalization
• Authors: Hao Yan, Yuhong Guo,
• Abstract summary: Domain generalization aims at developing suitable learning algorithms in source training domains.<n>We present a novel two-stage approach called Context-Aware Self-Adaptation (CASA) for domain generalization.
• Score: 32.094290282897894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain generalization aims at developing suitable learning algorithms in source training domains such that the model learned can generalize well on a different unseen testing domain. We present a novel two-stage approach called Context-Aware Self-Adaptation (CASA) for domain generalization. CASA simulates an approximate meta-generalization scenario and incorporates a self-adaptation module to adjust pre-trained meta source models to the meta-target domains while maintaining their predictive capability on the meta-source domains. The core concept of self-adaptation involves leveraging contextual information, such as the mean of mini-batch features, as domain knowledge to automatically adapt a model trained in the first stage to new contexts in the second stage. Lastly, we utilize an ensemble of multiple meta-source models to perform inference on the testing domain. Experimental results demonstrate that our proposed method achieves state-of-the-art performance on standard benchmarks.

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