Learning to Learn Domain-invariant Parameters for Domain Generalization
This addresses the problem of poor generalization to unknown test data for practitioners in machine learning, though it appears incremental as it builds on existing domain generalization approaches.
The paper tackles domain shift in deep neural networks by proposing a method to learn domain-invariant parameters for domain generalization, achieving state-of-the-art performance on two benchmarks.
Due to domain shift, deep neural networks (DNNs) usually fail to generalize well on unknown test data in practice. Domain generalization (DG) aims to overcome this issue by capturing domain-invariant representations from source domains. Motivated by the insight that only partial parameters of DNNs are optimized to extract domain-invariant representations, we expect a general model that is capable of well perceiving and emphatically updating such domain-invariant parameters. In this paper, we propose two modules of Domain Decoupling and Combination (DDC) and Domain-invariance-guided Backpropagation (DIGB), which can encourage such general model to focus on the parameters that have a unified optimization direction between pairs of contrastive samples. Our extensive experiments on two benchmarks have demonstrated that our proposed method has achieved state-of-the-art performance with strong generalization capability.