Cluster Alignment with a Teacher for Unsupervised Domain Adaptation (1903.09980v2)

Abstract: Deep learning methods have shown promise in unsupervised domain adaptation, which aims to leverage a labeled source domain to learn a classifier for the unlabeled target domain with a different distribution. However, such methods typically learn a domain-invariant representation space to match the marginal distributions of the source and target domains, while ignoring their fine-level structures. In this paper, we propose Cluster Alignment with a Teacher (CAT) for unsupervised domain adaptation, which can effectively incorporate the discriminative clustering structures in both domains for better adaptation. Technically, CAT leverages an implicit ensembling teacher model to reliably discover the class-conditional structure in the feature space for the unlabeled target domain. Then CAT forces the features of both the source and the target domains to form discriminative class-conditional clusters and aligns the corresponding clusters across domains. Empirical results demonstrate that CAT achieves state-of-the-art results in several unsupervised domain adaptation scenarios.

• The paper introduces CAT, a method that improves domain adaptation by aligning class-conditional feature clusters, achieving tighter alignment than RevGrad and MSTN.
• CAT uses a novel matching loss and confidence-thresholding to refine training, resulting in improved test accuracy and faster convergence across datasets.
• Empirical evaluations on tasks like SVHN to MNIST demonstrate that CAT outperforms existing methods in creating distinct and reliable feature spaces.

A Review of "Cluster Alignment with a Teacher for Unsupervised Domain Adaptation"

The paper "Cluster Alignment with a Teacher for Unsupervised Domain Adaptation" introduces a method called Cluster Alignment with a Teacher (CAT) to address the challenge of domain adaptation in the context of unsupervised learning. Domain adaptation remains a notable area of interest due to the increasing need for models that maintain performance across different domains without requiring labeled target data.

Key Contributions

The authors propose CAT, which emphasizes aligning class-conditional distributions between the source and target domains more tightly than traditional methods. The main concept underlying CAT is to create class-conditional clusters that are compact and well-separated. It contrasts with methods like RevGrad and MSTN that may result in misaligned clusters, notably where '0' images from the SVHN are mismatched with '1' images in the MNIST dataset.

CAT utilizes a combination of RevGrad with a novel matching loss $\mathcal{L}_a$, which focuses on achieving discriminative cluster alignment. The paper provides empirical evidence that reveals CAT's capabilities to surpass existing methods in constructing meaningful and distinguishable feature spaces across various domain adaptation tasks.

Experimental Evaluation

The paper conducts comprehensive experiments on datasets such as SVHN to MNIST, MNIST to USPS, Office-31, and ImageCLEF-DA. Visualization with t-SNE shows that CAT and the combination CAT+rRevGrad form more distinguishable feature spaces than their counterparts. For instance, on the challenge task of SVHN to MNIST, CAT delivers tighter and more aligned clusters compared to RevGrad and MSTN, as depicted in their feature space visualizations.

Moreover, quantitative analyses using Jensen-Shannon divergence (JSD) demonstrate CAT's ability to substantially enhance the convergence compared to RevGrad. The incorporation of CAT shows a positive effect on the alignment of marginal distributions between domains, evidenced by decreased divergence and increased test accuracy across iterations.

Confidence-Thresholding and Convergence

A significant innovation in the paper is the confidence-thresholding technique. This technique selects samples with higher classification confidence progressively during training, thus refining the domain adaptation process. The effectiveness of this method is evident in tasks like SVHN to MNIST, where cluster stability improves significantly over approximately 15,000 iterations.

Convergence metrics illustrated in the paper indicate that CAT reaches and maintains higher accuracy when pairing with classifiers such as AlexNet, as compared to RevGrad alone. The convergence graphs confirm that CAT provides not just faster, but also more reliable performance across tasks.

Practical Implications and Future Directions

This work underscores the potential of class-conditional alignment in unsupervised domain adaptation tasks. In practical terms, CAT could be applied to scenarios requiring robust performance across diverse domains without access to exhaustive labeled data in each new domain. For future developments, there is potential to explore CAT's integration with other state-of-the-art models and architectures to enrich its application scope further.

Furthermore, extending such strategies to semi-supervised or supervised contexts, where limited target labels are available, could yield interesting insights into improving domain alignment techniques. The adaptability of CAT across various architectures like AlexNet and ResNet-50 marks it as a promising candidate for further exploration in domain transfer learning.

In conclusion, the paper presents a valuable framework in the field of unsupervised domain adaptation, fusing theoretical and empirical strengths to propose an effective method for improving cross-domain class-conditional distributions alignment.