Aligning Domain-specific Distribution and Classifier for Cross-domain Classification from Multiple Sources (2201.01003v1)

Abstract: While Unsupervised Domain Adaptation (UDA) algorithms, i.e., there are only labeled data from source domains, have been actively studied in recent years, most algorithms and theoretical results focus on Single-source Unsupervised Domain Adaptation (SUDA). However, in the practical scenario, labeled data can be typically collected from multiple diverse sources, and they might be different not only from the target domain but also from each other. Thus, domain adapters from multiple sources should not be modeled in the same way. Recent deep learning based Multi-source Unsupervised Domain Adaptation (MUDA) algorithms focus on extracting common domain-invariant representations for all domains by aligning distribution of all pairs of source and target domains in a common feature space. However, it is often very hard to extract the same domain-invariant representations for all domains in MUDA. In addition, these methods match distributions without considering domain-specific decision boundaries between classes. To solve these problems, we propose a new framework with two alignment stages for MUDA which not only respectively aligns the distributions of each pair of source and target domains in multiple specific feature spaces, but also aligns the outputs of classifiers by utilizing the domain-specific decision boundaries. Extensive experiments demonstrate that our method can achieve remarkable results on popular benchmark datasets for image classification.

Aligning Domain-specific Distribution and Classifier for Cross-domain Classification from Multiple Sources

The paper authored by Zhu, Zhuang, and Wang addresses the complexities involved in Multi-source Unsupervised Domain Adaptation (MUDA), an area that holds significant practical relevance due to the prevalence of scenarios involving diverse source domains. Unlike Single-source Unsupervised Domain Adaptation (SUDA), where adaptation is performed using data from a single source, MUDA must contend with the inherent variabilities and discrepancies among data from multiple source domains, each potentially differing from the target domain as well as from each other.

Proposed Framework

The authors propose a novel two-stage alignment framework designed to overcome the limitations of existing MUDA methods. This framework introduces two key alignment stages, each focused on different aspects of the domain adaptation problem:

1. Domain-specific Distribution Alignment: This stage aims to learn domain-invariant representations by aligning the distributions of each source-target domain pair in separate feature spaces. The framework employs Maximum Mean Discrepancy (MMD) as the metric for distribution alignment, leveraging multiple domain-specific feature extractors to accommodate the diversity among source domains.
2. Classifier Alignment: The second stage addresses the challenge posed by domain-specific decision boundaries. It seeks to harmonize the outputs of domain-specific classifiers to ensure consistent classification results even for target samples near these boundaries. This is achieved by minimizing the discrepancies among classifiers' probabilistic outputs using a simple absolute-value loss function.

Empirical Evaluation

The effectiveness of the proposed method is rigorously evaluated on several benchmark datasets, namely Office-31, ImageCLEF-DA, and Office-Home. The empirical results clearly indicate that the proposed framework, termed the Multiple Feature Spaces Adaptation Network (MFSAN), outperforms traditional SUDA approaches, baseline thick-adaptation strategies, and other contemporary MUDA solutions.

Key results demonstrate that MFSAN significantly closes the performance gap between source-specific classifiers by aligning their outputs, and achieves superior overall classification accuracy across transfer tasks. The integration of domain-specific distribution and classifier alignment proves critical, as ablation studies reveal that each component contributes to the framework's success.

Implications and Future Work

The implications of this research are noteworthy for both the theoretical and applied domains of AI and machine learning. The method offers a clear pathway to enhance model performance in scenarios where multiple source domains are available, which is often the case in real-world applications such as medical imaging or cross-cultural sentiment analysis.

The proposed framework also opens several avenues for future research. Given the flexible nature of MFSAN, an exploration of alternative alignment techniques (e.g., adversarial training) could yield further improvements. Additionally, extending the methodology to accommodate semi-supervised variations or domain generalization scenarios, where the presence of label noise or unseen domains are factors, could increase its applicability.

This paper underscores the potential of a nuanced, multi-directional approach in domain adaptation, moving towards models that are not only robust in performance across diverse domains but also agile enough to integrate new sources of data seamlessly.