Unsupervised Domain Adaptation with Progressive Domain Augmentation
This addresses the challenge of adapting models across domains with limited labeled data, but it appears incremental as it builds on existing domain adaptation methods.
The paper tackles the problem of unsupervised domain adaptation when there is significant divergence between source and target domains, achieving state-of-the-art performance on multiple tasks.
Domain adaptation aims to exploit a label-rich source domain for learning classifiers in a different label-scarce target domain. It is particularly challenging when there are significant divergences between the two domains. In the paper, we propose a novel unsupervised domain adaptation method based on progressive domain augmentation. The proposed method generates virtual intermediate domains via domain interpolation, progressively augments the source domain and bridges the source-target domain divergence by conducting multiple subspace alignment on the Grassmann manifold. We conduct experiments on multiple domain adaptation tasks and the results shows the proposed method achieves the state-of-the-art performance.