A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation (2003.02541v2)

Abstract: This work addresses the unsupervised domain adaptation problem, especially in the case of class labels in the target domain being only a subset of those in the source domain. Such a partial transfer setting is realistic but challenging and existing methods always suffer from two key problems, negative transfer and uncertainty propagation. In this paper, we build on domain adversarial learning and propose a novel domain adaptation method BA$^3$US with two new techniques termed Balanced Adversarial Alignment (BAA) and Adaptive Uncertainty Suppression (AUS), respectively. On one hand, negative transfer results in misclassification of target samples to the classes only present in the source domain. To address this issue, BAA pursues the balance between label distributions across domains in a fairly simple manner. Specifically, it randomly leverages a few source samples to augment the smaller target domain during domain alignment so that classes in different domains are symmetric. On the other hand, a source sample would be denoted as uncertain if there is an incorrect class that has a relatively high prediction score, and such uncertainty easily propagates to unlabeled target data around it during alignment, which severely deteriorates adaptation performance. Thus we present AUS that emphasizes uncertain samples and exploits an adaptive weighted complement entropy objective to encourage incorrect classes to have uniform and low prediction scores. Experimental results on multiple benchmarks demonstrate our BA$^3$US surpasses state-of-the-arts for partial domain adaptation tasks. Code is available at \url{https://github.com/tim-learn/BA3US}.

• The paper presents BA³US, which combines Balanced Adversarial Alignment and Adaptive Uncertainty Suppression to mitigate negative transfer in partial domain adaptation.
• It equalizes class distributions by augmenting the target domain with selected source samples, effectively transforming PDA into a standard UDA problem.
• Empirical evaluations on Office31, Office-Home, and ImageNet-Caltech datasets show that BA³US outperforms current state-of-the-art methods.

A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation

The paper under discussion introduces a novel approach named BA$^3$US (Balanced Adversarial Alignment and Adaptive Uncertainty Suppression) to address challenges specific to partial domain adaptation (PDA). PDA poses significant difficulties because it deals with the scenario where the class labels in the target domain constitute only a subset of those in the source domain. This situation commonly results in issues of negative transfer due to class mismatch and uncertainty propagation during the adaptation process.

The authors propose an innovative strategy built on domain adversarial learning to mitigate these challenges. The primary techniques put forward in the paper are Balanced Adversarial Alignment (BAA) and Adaptive Uncertainty Suppression (AUS). The BAA technique seeks to counteract the negative transfer problem by ensuring symmetry in label distributions across the domains. It does so by augmenting the smaller target domain with a random subset of source domain samples to equalize the class distribution. This equalization helps in transforming the complex PDA problem into a traditional unsupervised domain adaptation (UDA) problem, where the label spaces are identical between the domains. In parallel, the AUS technique addresses uncertainty propagation, a problem where high uncertainty in source domain predictions negatively impacts the target domain. By employing an adaptive weighted complement entropy objective on uncertain source samples, the uncertain labels are suppressed, thereby improving the adaptation performance.

The empirical evaluation demonstrates the effectiveness of BA$^3$US on multiple benchmarks, including the Office31, Office-Home, and ImageNet-Caltech datasets. The BA$^3$US approach consistently outperforms the state-of-the-art PDA methods, showcasing its robustness and efficiency.

Practically, BA$^3$US represents a step forward in tackling PDA tasks where class distribution disparities are prevalent, offering an efficient solution without necessitating complex model modifications or high computational costs. It opens avenues for applying domain adaptation methods in real-world scenarios where partial overlap between source and target data is more common.

Theoretically, the introduction of techniques like BAA and AUS in adversarial learning frameworks extends the adaptability and precision of domain adaptation methodologies. These contributions are likely to prompt further exploration and refinement of domain adaptation techniques, especially those relevant to settings with inherent domain-specific biases.

The paper also suggests the potential broader application of the uncertainty suppression technique to closed-set domain adaptation, as demonstrated by its ability to enhance standard UDA tasks. This flexibility positions BA$^3$US not only as a specialized tool for PDA but as a potentially versatile approach for various domain adaptation challenges.

Future research in artificial intelligence and machine learning could expand upon this work by exploring other methods to balance domain label distributions, incorporating different entropy-based metrics, or simplifying the adaptive suppression mechanisms. Such advancements could contribute to more generic and scalable solutions for domain adaptation tasks across diverse applications.