AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation (2106.04732v2)

Abstract: We extend semi-supervised learning to the problem of domain adaptation to learn significantly higher-accuracy models that train on one data distribution and test on a different one. With the goal of generality, we introduce AdaMatch, a method that unifies the tasks of unsupervised domain adaptation (UDA), semi-supervised learning (SSL), and semi-supervised domain adaptation (SSDA). In an extensive experimental study, we compare its behavior with respective state-of-the-art techniques from SSL, SSDA, and UDA on vision classification tasks. We find AdaMatch either matches or significantly exceeds the state-of-the-art in each case using the same hyper-parameters regardless of the dataset or task. For example, AdaMatch nearly doubles the accuracy compared to that of the prior state-of-the-art on the UDA task for DomainNet and even exceeds the accuracy of the prior state-of-the-art obtained with pre-training by 6.4% when AdaMatch is trained completely from scratch. Furthermore, by providing AdaMatch with just one labeled example per class from the target domain (i.e., the SSDA setting), we increase the target accuracy by an additional 6.1%, and with 5 labeled examples, by 13.6%.

• The paper introduces a unified framework that integrates semi-supervised learning, unsupervised domain adaptation, and semi-supervised domain adaptation with consistent hyperparameters.
• It refines the FixMatch algorithm with random logit interpolation, relative confidence thresholding, and modified distribution alignment to improve domain performance.
• Through extensive benchmarks on DigitFive and DomainNet, AdaMatch achieves state-of-the-art accuracy while reducing hyperparameter tuning complexity.

An Overview of "AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation"

The paper "AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation" addresses the challenge of learning effective models across different but related tasks and datasets, particularly when labeled data is scarce. The authors introduce AdaMatch, a sophisticated algorithm designed to extend semi-supervised learning (SSL) to unsupervised domain adaptation (UDA) and semi-supervised domain adaptation (SSDA), using a consistent set of hyperparameters across various settings.

Key Contributions

1. Unified Learning Framework: AdaMatch integrates multiple learning and adaptation paradigms—UDA, SSL, and SSDA—into a single framework. This unification enables the use of the same hyperparameters regardless of the dataset, which simplifies the training process and reduces the computational overhead associated with hyperparameter tuning.
2. Algorithmic Extensions: AdaMatch refines the FixMatch algorithm by incorporating:
   • Random Logit Interpolation: This technique interpolates between source and target domain logits to better align domain-specific batch normalization statistics.
   • Relative Confidence Thresholding: Adjusting the confidence threshold relative to top-1 prediction confidence on labeled source data allows better use of unlabeled target data.
   • Modified Distribution Alignment: Building on ReMixMatch, AdaMatch adjusts target domain pseudo-label distributions to align with source domain distributions, regularizing classifier predictions to reduce class imbalance effects.
3. State-of-the-Art Performance: Through extensive experimentation on benchmarks such as DigitFive and DomainNet, AdaMatch is shown to either match or surpass the accuracy of existing state-of-the-art methods. This performance is especially notable on DomainNet, where AdaMatch improves UDA accuracy significantly, even doubling previous results in some settings.
4. Comprehensive Evaluation: A thorough experimental design evaluates AdaMatch against baseline models across a breadth of tasks. Additionally, democratizing research by adapting the dataset resolution without major accuracy loss makes benchmarking more accessible to the research community with different computational capabilities.

Implications and Future Scope

AdaMatch demonstrates that leveraging unlabeled data effectively can dramatically improve model robustness across domain shifts, a critical insight for practical machine learning systems often faced with distribution differences between training and deployment environments. This paper provides a compelling case for continued integration of SSL techniques into domain adaptation tasks, benefiting applications ranging from image recognition to natural language processing.

Theoretically, the introduction of uniform hyperparameter settings across multiple learning paradigms demonstrates the potential for broader applicability, easing the adoption of such methods in various industrial and research scenarios. However, the dependence on labeled source distributions for pseudo-label alignment suggests future work should probe techniques that can adapt more flexibly to unknown or dynamic target distributions.

Conclusion

This paper contributes a substantial advancement in the integration of semi-supervised learning and domain adaptation, presenting a practical solution in AdaMatch that addresses the limitations of existing fragmented approaches. By carefully considering the differences in domain distributions and adapting SSL techniques for domain adaptation, AdaMatch sets a new benchmark for accuracy and efficiency across shifted domains, positioning it as an essential tool in the machine learning practitioner's toolkit. As large-scale datasets continue to evolve, algorithms like AdaMatch that unify learning contexts and provide reliable performance irrespective of data shifts will become increasingly vital.