Learning to Learn Domain-invariant Parameters for Domain Generalization

• URL: http://arxiv.org/abs/2211.04582v1
• Date: Fri, 4 Nov 2022 07:19:34 GMT
• Title: Learning to Learn Domain-invariant Parameters for Domain Generalization
• Authors: Feng Hou, Yao Zhang, Yang Liu, Jin Yuan, Cheng Zhong, Yang Zhang, Zhongchao Shi, Jianping Fan, Zhiqiang He
• Abstract summary: Domain generalization (DG) aims to overcome this issue by capturing domain-invariant representations from source domains. We propose two modules of Domain Decoupling and Combination (DDC) and Domain-invariance-guided Backpropagation (DIGB) Our proposed method has achieved state-of-the-art performance with strong generalization capability.
• Score: 29.821634033299855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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