Domain Generalization Using a Mixture of Multiple Latent Domains

Understand

When domains, which represent underlying data distributions, vary during training and testing processes, deep neural networks suffer a drop in their performance.

• Domain generalization allows improvements in the generalization performance for unseen target domains by using multiple source domains.
• Conventional methods assume that the domain to which each sample belongs is known in training.
• However, many datasets, such as those collected via web crawling, contain a mixture of multiple latent domains, in which the domain of each sample is unknown.