Transfer Feature Learning with Joint Distribution Adaptation

TLDR

Transfer learning leverages labeled source data to build accurate target classifiers, yet most existing methods fail to simultaneously reduce both marginal and conditional distribution gaps between domains. This work introduces Joint Distribution Adaptation (JDA) as a novel transfer learning framework. JDA jointly adapts marginal and conditional distributions through a principled dimensionality‑reduction procedure, yielding a robust feature representation that mitigates substantial distribution differences. Extensive experiments demonstrate that JDA significantly outperforms several state‑of‑the‑art methods on four cross‑domain image classification tasks.

Abstract

Transfer learning is established as an effective technology in computer vision for leveraging rich labeled data in the source domain to build an accurate classifier for the target domain. However, most prior methods have not simultaneously reduced the difference in both the marginal distribution and conditional distribution between domains. In this paper, we put forward a novel transfer learning approach, referred to as Joint Distribution Adaptation (JDA). Specifically, JDA aims to jointly adapt both the marginal distribution and conditional distribution in a principled dimensionality reduction procedure, and construct new feature representation that is effective and robust for substantial distribution difference. Extensive experiments verify that JDA can significantly outperform several state-of-the-art methods on four types of cross-domain image classification problems.