Transferability versus Discriminability: Joint Probability Distribution Adaptation (JPDA)
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Transfer learning makes use of data or knowledge in one task to help solve a different, yet related, task. Many existing TL approaches are based on a joint probability distribution metric, which is a weighted sum of the marginal distribution and the conditional distribution; however, they optimize the two distributions independently, and ignore their intrinsic dependency. This paper proposes a novel and frustratingly easy Joint Probability Distribution Adaptation (JPDA) approach, to replace the frequently-used joint maximum mean discrepancy metric in transfer learning. During the distribution adaptation, JPDA improves the transferability between the source and the target domains by minimizing the joint probability discrepancy of the corresponding class, and also increases the discriminability between different classes by maximizing their joint probability discrepancy. Experiments on six image classification datasets demonstrated that JPDA outperforms several state-of-the-art metric-based transfer learning approaches.
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