Improving Domain Generalization with Multi-Scale and Multi-Layer Contrastive Learning
The author argues that deep neural networks can improve domain generalization by utilizing multi-layer and multi-scaled representations, along with a novel contrastive loss function. This approach aims to disentangle representations and learn domain-invariant attributes of images.