Transfer Learning with Dynamic Distribution Adaptation (1909.08531v1)

Abstract: Transfer learning aims to learn robust classifiers for the target domain by leveraging knowledge from a source domain. Since the source and the target domains are usually from different distributions, existing methods mainly focus on adapting the cross-domain marginal or conditional distributions. However, in real applications, the marginal and conditional distributions usually have different contributions to the domain discrepancy. Existing methods fail to quantitatively evaluate the different importance of these two distributions, which will result in unsatisfactory transfer performance. In this paper, we propose a novel concept called Dynamic Distribution Adaptation (DDA), which is capable of quantitatively evaluating the relative importance of each distribution. DDA can be easily incorporated into the framework of structural risk minimization to solve transfer learning problems. On the basis of DDA, we propose two novel learning algorithms: (1) Manifold Dynamic Distribution Adaptation (MDDA) for traditional transfer learning, and (2) Dynamic Distribution Adaptation Network (DDAN) for deep transfer learning. Extensive experiments demonstrate that MDDA and DDAN significantly improve the transfer learning performance and setup a strong baseline over the latest deep and adversarial methods on digits recognition, sentiment analysis, and image classification. More importantly, it is shown that marginal and conditional distributions have different contributions to the domain divergence, and our DDA is able to provide good quantitative evaluation of their relative importance which leads to better performance. We believe this observation can be helpful for future research in transfer learning.

• The paper introduces Dynamic Distribution Adaptation (DDA) as a major innovation to weigh marginal and conditional alignments dynamically for robust transfer learning.
• It proposes two models, MDDA and DDAN, that leverage manifold learning and deep neural networks to optimize feature representations across domains.
• Empirical evaluations on benchmarks like digit recognition and sentiment analysis demonstrate significant performance gains over state-of-the-art transfer learning methods.

Transfer Learning with Dynamic Distribution Adaptation

The paper "Transfer Learning with Dynamic Distribution Adaptation" addresses a significant challenge in transfer learning: the effective adaptation between source and target domains that generally exhibit distinct statistical distributions. This discrepancy, if unaddressed, often results in suboptimal performance when a model trained on the source domain is applied to the target domain. Existing methods predominantly focus on aligning either marginal or conditional distributions, typically attributing equal importance to both. However, this paper introduces a novel approach known as Dynamic Distribution Adaptation (DDA), which dynamically assesses and adapts the relative importance of marginal and conditional distributions to improve transfer learning outcomes.

Research Methodology

The core contribution of the paper is the DDA framework, which quantitatively evaluates the significance of marginal versus conditional distribution alignment during the transfer process. The authors incorporate DDA into structural risk minimization, proposing two algorithms: Manifold Dynamic Distribution Adaptation (MDDA) for traditional learning scenarios and Dynamic Distribution Adaptation Network (DDAN) for deep learning contexts. These models dynamically adjust their feature representations to minimize domain divergence and enhance target domain classification performance.

Key Algorithms

1. Manifold Dynamic Distribution Adaptation (MDDA): This algorithm operates in a Grassmann manifold, leveraging its geometrical properties to mitigate feature distortion during domain adaptation. It addresses both marginal and conditional distribution alignment adaptively, optimizing the feature representations through manifold learning techniques.
2. Dynamic Distribution Adaptation Network (DDAN): A deep learning extension of DDA, DDAN integrates dynamic adaptation into a neural network architecture, allowing for end-to-end learning of transferable features and classifiers. It iteratively updates the importance of distribution components while minimizing a joint loss of prediction error and distribution divergence.

Empirical Evaluation

The authors validate the efficacy of MDDA and DDAN on several benchmarks, including digit recognition, sentiment analysis, and image classification. Experiments demonstrate that these methods achieve superior performance over state-of-the-art traditional and deep transfer learning techniques. Notably, MDDA and DDAN are shown to significantly improve classification accuracy by dynamically balancing the adaptation between marginal and conditional distributions based on domain similarities.

Implications and Future Directions

The findings underscore the necessity of weighted distribution adaptation in transfer learning, challenging the prevalent assumption of equal distribution importance. Practically, DDA-based models offer a robust alternative for tasks characterized by large domain shifts and complex distributional structures. Theoretically, this research opens up new avenues for understanding the nuanced interactions between feature alignment and distribution shifts in transfer learning.

Moving forward, exploration into adaptive factor optimization, beyond current heuristics or reliance on $\mathcal{A}$-distance, holds potential for enhancing dynamic alignment strategies. Additionally, integrating DDA with adversarial learning frameworks could further refine its capability to accommodate broader and more challenging domain adaptation scenarios.

Overall, this paper contributes significantly to the field of transfer learning by providing a more informed and flexible framework for domain adaptation, paving the way for refined methodologies that account for complex distributional dynamics.