通过上下文自适应提升模型在未知领域下的泛化能力。
Context-Aware Self-Adaptation for Domain Generalization
- 利用批量特征均值等上下文信息实现模型自适应调整
- 在多个标准基准上达到当前最优性能
- 适合需要跨领域泛化的实际应用场景
领域泛化旨在设计适用于源域训练的算法，使模型在不同未见的目标域上仍能良好泛化。本文提出一种名为上下文感知自适应（CASA）的两阶段新方法。CASA 模拟近似元泛化场景，并引入自适应模块，将预训练的元源模型调整至元目标域，同时保持其在元源域上的预测能力。自适应核心思想是利用批量特征均值等上下文信息作为领域知识，自动调整第一阶段训练的模型以适应第二阶段的新上下文。最后，通过集成多个元源模型对测试域进行推理。实验结果表明，所提方法在标准基准上达到领先性能。
原文摘要 · Abstract (English)
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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