Active Learning for Domain Adaptation: An Energy-Based Approach (2112.01406v3)

Abstract: Unsupervised domain adaptation has recently emerged as an effective paradigm for generalizing deep neural networks to new target domains. However, there is still enormous potential to be tapped to reach the fully supervised performance. In this paper, we present a novel active learning strategy to assist knowledge transfer in the target domain, dubbed active domain adaptation. We start from an observation that energy-based models exhibit \textit{free energy biases} when training (source) and test (target) data come from different distributions. Inspired by this inherent mechanism, we empirically reveal that a simple yet efficient energy-based sampling strategy sheds light on selecting the most valuable target samples than existing approaches requiring particular architectures or computation of the distances. Our algorithm, Energy-based Active Domain Adaptation (EADA), queries groups of target data that incorporate both domain characteristic and instance uncertainty into every selection round. Meanwhile, by aligning the free energy of target data compact around the source domain via a regularization term, domain gap can be implicitly diminished. Through extensive experiments, we show that EADA surpasses state-of-the-art methods on well-known challenging benchmarks with substantial improvements, making it a useful option in the open world. Code is available at https://github.com/BIT-DA/EADA.

• The paper introduces EADA, a novel energy-based active learning method to enhance domain adaptation by strategically selecting unlabeled target samples for labeling.
• EADA utilizes free energy measures and instance uncertainty (MvSM) to identify representative samples near decision boundaries that are critical for minimizing domain shifts.
• Experimental results on datasets like VisDA-2017 and Office-Home validate that EADA significantly improves accuracy with minimal labeled data, outperforming existing methods.

Active Learning for Domain Adaptation: An Energy-Based Approach

The paper "Active Learning for Domain Adaptation: An Energy-Based Approach" introduces a novel method for domain adaptation facilitated by active learning. Despite notable progress in unsupervised domain adaptation, there exists an uncharted potential for achieving the performance levels seen in fully supervised models. The paper proposes an innovative algorithm, Energy-based Active Domain Adaptation (EADA), which improves the effectiveness of domain adaptation by intelligently selecting which unlabeled target data should be labeled to enhance model performance substantially.

Overview of EADA

The authors establish the inadequacy of previous methods in effectively bridging the gap between domain learning and adaptation due to the distinct distribution biases between the source and target domains. The proposed EADA leverages energy-based models (EBMs), which provide significant advantages in such scenarios—the basic premise underlying the method is that EBMs demonstrate free energy biases, which can be used as a surrogate for identifying domain characteristic and uncertainty.

The EADA algorithm queries and selects target samples based on two main metrics:

1. Domain Characteristic: Free energy is used to assess how representative a sample is of the target domain. Samples with higher free energy are selected since they are more dissimilar to the source domain.
2. Instance Uncertainty: A Min-versus-Second-Min (MvSM) strategy calculates the difference between the minimum energy and the second-minimum energy output, highlighting samples where the model demonstrates uncertainty. Such samples are typically found alongside decision boundaries, making them crucial for improving model generalization.

Additionally, the paper introduces a free energy alignment loss to implicitly reduce the domain shift, which complements the active selection strategy by aligning the free energy distribution between the source and target domains.

Numerical Outcomes and Implications

Through extensive experimentation across image classification datasets such as VisDA-2017, Office-Home, and Office-31, as well as in semantic segmentation using GTAV to Cityscapes, EADA showed substantial improvements over existing methods. For instance, on the VisDA-2017 dataset with merely 5% of the target samples labeled, EADA achieved an accuracy of 88.3%, a marked improvement over leading alternatives, including AADA and CLUE.

The paper's implications extend beyond numerical performance; these results underscore the importance of selecting representative and informative samples for labeling. The energy-based methodology sheds light on efficient sample selection that both considers domain-specific characteristics and minimizes computational overhead, making EADA practically useful in open-world scenarios where labeled data acquisition is costly.

Future Prospects

The promising results and comprehensive analysis provided by the paper open avenues for further research into active learning strategies tailored for domain adaptation. Upcoming work may incorporate adaptive systems for continuously refining selection strategies based on dynamically evolving data distributions. Additionally, integrating EADA into broader AI frameworks addressing real-time applications could catalyze advances in domains such as autonomous driving and medical diagnostics—fields critically dependent on large volumes of unlabeled target data.

Overall, the paper presents a significant advance in domain adaptation, offering a method that is both theoretically and empirically validated. It encourages future investigation into energy-based models and active learning as synergistic forces in improving machine learning adaptation performance across domains. By leveraging these insights, researchers can further enhance the reliability and versatility of deep learning models in diverse applications.