Geodesy

Gene

Benchmark (surveying)

Discriminative model

Complement (music)

Operating system

Set (abstract data type)

Biochemistry

Computer science

Code (set theory)

Complementation

Source code

Upper and lower bounds

Pattern recognition (psychology)

Mathematics

Geography

Domain (mathematical analysis)

Mathematical analysis

Artificial intelligence

Chemistry

Programming language

Generalization

Machine learning

Phenotype

Domain generalization (DG) aims to generalize a model trained on multiple source (i.e., training) domains to a distributionally different target (i.e., test) domain. In contrast to the conventional DG that strictly requires the availability of multiple source domains, this paper considers a more realistic yet challenging scenario, namely Single Domain Generalization (Single-DG), where only one source domain is available for training. In this scenario, the limited diversity may jeopardize the model generalization on unseen target domains. To tackle this problem, we propose a style-complement module to enhance the generalization power of the model by synthesizing images from diverse distributions that are complementary to the source ones. More specifically, we adopt a tractable upper bound of mutual information (MI) between the generated and source samples and perform a two-step optimization iteratively: (1) by minimizing the MI upper bound approximation for each sample pair, the generated images are forced to be diversified from the source samples; (2) subsequently, we maximize the MI between the samples from the same semantic category, which assists the network to learn discriminative features from diverse-styled images. Extensive experiments on three benchmark datasets demonstrate the superiority of our approach, which surpasses the state-of-the-art single-DG methods by up to 25.14%. The code will be publicly available at https://github.com/BUserName/Learning_to_diversify

Learning to Diversify for Single Domain Generalization