CT-DANN

Unsupervised domain adaptation aims at leveraging supervision from an annotated source domain for performing tasks like classification/segmentation on an unsupervised target domain. However, a large enough related dataset with clean annotations may not be always available in real scenarios, since annotations are usually obtained from crowd sourcing, and thus are noisy. Here, we consider a more realistic and challenging setting, wild unsupervised domain adaptation (WUDA), where the source domain annotations can be noisy. Standard domain adaptation approaches which directly use these noisy source labels and the unlabeled targets for the domain adaptation task perform poorly, due to severe negative transfer from the noisy source domain. In this work, we propose a novel end-to-end framework, termed CT-DANN (Co-teaching meets DANN), which seamlessly integrates a state-of-the-art approach for handling noisy labels (Co-teaching) with a standard domain adaptation framework (DANN). CT-DANN effectively utilizes all the source samples after accounting for both their noisy labels as well as transferability with respect to the target domain. Extensive experiments on three benchmark datasets with different types and levels of noise and comparison with state-of-the-art WUDA approach justify the effectiveness of the proposed framework.