Transfer Learning with Label Noise
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Transfer learning aims to improve learning in the target domain with limited training data by borrowing knowledge from a related but different source domain with sufficient labeled data. To reduce the distribution shift between source and target domains, recent methods have focused on exploring invariant representations that have similar distributions across domains. However, existing methods assume that the labels in the source domain are uncontaminated, while in reality, we often only have access to a source domain with noisy labels. In this paper, we first analyze the effects of label noise in various transfer learning scenarios in which the data distribution is assumed to change in different ways. We find that although label noise has no effect on the invariant representation learning in the covariate shift scenario, it has adverse effects on the learning process in the more general target/conditional shift scenarios. To solve this problem, we propose a new transfer learning method to learn invariant representations in the presence of label noise, which also simultaneously estimates the label distributions in the target domain. Experimental results on both synthetic and real-world data verify the effectiveness of the proposed method.
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