Context-Aware Self-Adaptation for Domain Generalization
This addresses the problem of model generalization to unseen domains for machine learning practitioners, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles domain generalization by proposing a two-stage approach called Context-Aware Self-Adaptation (CASA), which uses contextual information and self-adaptation to adjust pre-trained models to unseen domains, achieving state-of-the-art performance on standard benchmarks.
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.