Towards Robust Cross-domain Image Understanding with Unsupervised Noise Removal

TL;DR

This paper introduces a noise-tolerant domain adaptation method that improves cross-domain image understanding by effectively removing noisy source data and aligning semantic structures, especially in scenarios with label scarcity and noisy labels.

Contribution

The proposed method leverages Gaussian mixture models and cluster-level adversarial adaptation to enhance weakly supervised domain adaptation under noisy source conditions.

Findings

Significantly outperforms existing WSDA methods on general and medical image datasets.

Effectively identifies and removes noisy source data using a Gaussian mixture model.

Improves semantic alignment across domains with noisy labels.

Abstract

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.