Understanding Self-Training for Gradual Domain Adaptation (2002.11361v1)

Abstract: Machine learning systems must adapt to data distributions that evolve over time, in applications ranging from sensor networks and self-driving car perception modules to brain-machine interfaces. We consider gradual domain adaptation, where the goal is to adapt an initial classifier trained on a source domain given only unlabeled data that shifts gradually in distribution towards a target domain. We prove the first non-vacuous upper bound on the error of self-training with gradual shifts, under settings where directly adapting to the target domain can result in unbounded error. The theoretical analysis leads to algorithmic insights, highlighting that regularization and label sharpening are essential even when we have infinite data, and suggesting that self-training works particularly well for shifts with small Wasserstein-infinity distance. Leveraging the gradual shift structure leads to higher accuracies on a rotating MNIST dataset and a realistic Portraits dataset.

• The paper provides a novel theoretical framework for gradual self-training, quantifying error bounds during progressive domain shifts.
• It emphasizes the importance of regularization and label sharpening to ensure model stability even with infinite data.
• Empirical results on synthetic and real datasets demonstrate that gradual self-training significantly outperforms direct target adaptation.

Understanding Self-Training for Gradual Domain Adaptation

The paper, "Understanding Self-Training for Gradual Domain Adaptation," by Ananya Kumar, Tengyu Ma, and Percy Liang, focuses on adapting machine learning models to evolving data distributions, encapsulated in the concept of gradual domain adaptation. This scenario is particularly relevant in fields such as sensor networks, self-driving cars, and brain-machine interfaces, where data variability over time can degrade model performance.

Core Contributions and Theoretical Insights

1. Theoretical Framework: The authors establish foundational theoretical results for self-training in the setting of gradual domain adaptation. They introduce a theoretical framework elucidating the efficacy of self-training under gradual shifts, overcoming situations where direct adaptation to a distant target domain could lead to unbounded errors. This is achieved by offering the first non-vacuous upper bound on error during such shifts.
2. Algorithmic Insights: A critical insight from this paper is the significance of regularization and label sharpening. The findings suggest that these elements remain crucial even with infinite data, particularly when adjustments involve minor shifts characterized by a small Wasserstein-infinity distance.
3. Empirical Validation: The paper validates its theoretical claims through experiments on a rotating MNIST dataset and a realistic Portraits dataset. Leveraging gradual shift structures, the authors demonstrate enhanced accuracy, with gradual self-training notably surpassing direct target adaptation (77% to 84% accuracy on the Portraits dataset).

Methodological Rigour

• Margin Setting Analysis:

The research introduces a distribution-free margin setting using the Wasserstein-infinity distance. This approach acknowledges non-overlapping support between source and target domains, achieving improvements through gradual self-training evidenced by bounded error after multiple time steps.

• Gaussian Setting Exploration:

An idealized Gaussian setting explores conditions for achieving better-than-exponential error bounds, providing insights into optimal recovery for target distributions when distribution shifts are minor.

Key Results and Experimental Design

The experimental section underlines the practical implications of the proposed gradual self-training methodology. Across synthetic Gaussian, rotating MNIST, and realistic Portrait datasets, the results affirm the superiority of using gradual shift structures. Key observations include:

• Gradual self-training exceeds both direct target adaptation and self-training on pooled data, underscoring the advantages of leveraging the shift structure.
• Regularization remains crucial, and the accuracy gap persists despite increasing data, contrasting with traditional supervised learning scenarios.

Implications and Future Directions

The outcomes of this work have significant implications both theoretically and practically. The insights into regularization and label sharpening provide a nuanced understanding of self-training dynamics under domain shifts, indicating potential directions for future work in semi-supervised learning and domain adaptation. Practically, these findings can inform the deployment of machine learning systems in dynamic environments, potentially leading to more robust performance over time.

Conclusion

In conclusion, this paper offers a detailed examination of self-training for gradual domain adaptation, with valuable theoretical contributions and substantiated empirical evidence. The proposed methods and analyses pave the way for further research in optimizing model adaptation to gradual, real-world data shifts, offering a robust framework for future advances in AI applications across various disciplines.