Balanced Distribution Adaptation for Transfer Learning (1807.00516v1)

Abstract: Transfer learning has achieved promising results by leveraging knowledge from the source domain to annotate the target domain which has few or none labels. Existing methods often seek to minimize the distribution divergence between domains, such as the marginal distribution, the conditional distribution or both. However, these two distances are often treated equally in existing algorithms, which will result in poor performance in real applications. Moreover, existing methods usually assume that the dataset is balanced, which also limits their performances on imbalanced tasks that are quite common in real problems. To tackle the distribution adaptation problem, in this paper, we propose a novel transfer learning approach, named as Balanced Distribution \underline{A}daptation~(BDA), which can adaptively leverage the importance of the marginal and conditional distribution discrepancies, and several existing methods can be treated as special cases of BDA. Based on BDA, we also propose a novel Weighted Balanced Distribution Adaptation~(W-BDA) algorithm to tackle the class imbalance issue in transfer learning. W-BDA not only considers the distribution adaptation between domains but also adaptively changes the weight of each class. To evaluate the proposed methods, we conduct extensive experiments on several transfer learning tasks, which demonstrate the effectiveness of our proposed algorithms over several state-of-the-art methods.

• The paper presents a novel BDA method that adaptively balances marginal and conditional distributions using a balance factor μ.
• Experimental results show BDA achieves a 2.38% higher average accuracy than state-of-the-art methods on benchmark datasets.
• The extension, W-BDA, incorporates a weighting mechanism to address class imbalance, enhancing its applicability in real-world domain adaptation tasks.

Balanced Distribution Adaptation for Transfer Learning: An Academic Overview

The paper "Balanced Distribution Adaptation for Transfer Learning" introduces a novel approach to enhancing the efficacy of transfer learning by addressing the critical issues of distribution discrepancy and class imbalance between source and target domains. The approach, named Balanced Distribution Adaptation (BDA), and its extension, Weighted Balanced Distribution Adaptation (W-BDA), are proposed as solutions to these prevalent challenges in domain adaptation tasks.

Overview

Transfer learning has become an indispensable technique in situations where labeled data is scarce, capitalizing on knowledge from a well-labeled source domain to infer the target domain that lacks sufficient labeled data. The predominant strategy to minimize discrepancies in transfer learning involves reducing distribution divergence between datasets, primarily focusing on marginal and conditional distribution adaptations. However, existing methods predominantly treat these distribution distances equally, which may not align with real-world scenarios where dataset balance is often uneven. This paper proposes BDA to address these issues by adaptively balancing the importance of marginal and conditional distributions.

Methodology

BDA introduces a critical innovation by utilizing a balance factor, denoted as μ, to dynamically adjust the importance assigned to marginal and conditional distribution adaptations. This nuanced approach allows BDA to adapt to varying levels of similarity between source and target datasets. The theoretical underpinning of BDA is built on Maximum Mean Discrepancy (MMD) to empirically estimate distribution discrepancies. Furthermore, the authors extend BDA to W-BDA, which addresses class imbalance by implementing a weighting mechanism for different classes, thereby refining the accuracy of class prior estimations.

Experimental Results

The authors conduct extensive experiments on benchmark datasets such as USPS, MNIST, COIL20, and others to validate the proposed methods. BDA demonstrated superior accuracy compared to state-of-the-art methods like Joint Distribution Adaptation (JDA) and Transfer Component Analysis (TCA). Notably, BDA's average accuracy was higher than JDA by a margin of 2.38%, illustrating its enhanced adaptability to varying domain scenarios. W-BDA further outperformed BDA in tasks characterized by significant class imbalances, validating its efficacy in addressing another dimension of distributional challenges.

Implications and Future Directions

The introduction of the balance factor μ in BDA represents a significant theoretical advancement in transfer learning, providing a framework that can be fine-tuned to a given task's distribution characteristics. This adaptability is crucial, particularly in applications where the computational cost of retraining models with new distributions can be prohibitive. Furthermore, W-BDA's approach to class imbalance could potentially be extended and applied to other machine learning paradigms that wrestle with imbalanced data.

Anticipated future developments could include the automated estimation of μ to further integrate BDA and W-BDA into broader machine learning pipelines without extensive parameter tuning. Additionally, exploring more sophisticated kernel functions might offer improved generalization capabilities, especially in high-dimensional or highly non-linear problem spaces.

Conclusion

This paper contributes significantly to transfer learning by presenting robust methodologies to balance distribution adaptation and class imbalances. The experimental results highlight the effectiveness of BDA and W-BDA, exceeding existing methods in performance across various datasets. The proposed approaches expand the theoretical and practical toolkit for domain adaptation, paving the way for more efficient and effective applications in transfer learning as the field continues to evolve.