  Deep learning models usually require a large amount of labeled data to
achieve satisfactory performance. In multimedia analysis, domain adaptation
studies the problem of cross-domain knowledge transfer from a label rich source
domain to a label scarce target domain, thus potentially alleviates the
annotation requirement for deep learning models. However, we find that
contemporary domain adaptation methods for cross-domain image understanding
perform poorly when source domain is noisy. Weakly Supervised Domain Adaptation
(WSDA) studies the domain adaptation problem under the scenario where source
data can be noisy. Prior methods on WSDA remove noisy source data and align the
marginal distribution across domains without considering the fine-grained
semantic structure in the embedding space, which have the problem of class
misalignment, e.g., features of cats in the target domain might be mapped near
features of dogs in the source domain. In this paper, we propose a novel
method, termed Noise Tolerant Domain Adaptation, for WSDA. Specifically, we
adopt the cluster assumption and learn cluster discriminatively with class
prototypes in the embedding space. We propose to leverage the location
information of the data points in the embedding space and model the location
information with a Gaussian mixture model to identify noisy source data. We
then design a network which incorporates the Gaussian mixture noise model as a
sub-module for unsupervised noise removal and propose a novel cluster-level
adversarial adaptation method which aligns unlabeled target data with the less
noisy class prototypes for mapping the semantic structure across domains. We
conduct extensive experiments to evaluate the effectiveness of our method on
both general images and medical images from COVID-19 and e-commerce datasets.
The results show that our method significantly outperforms state-of-the-art
WSDA methods.
