Visual Domain Adaptation with Manifold Embedded Distribution Alignment (1807.07258v2)

Abstract: Visual domain adaptation aims to learn robust classifiers for the target domain by leveraging knowledge from a source domain. Existing methods either attempt to align the cross-domain distributions, or perform manifold subspace learning. However, there are two significant challenges: (1) degenerated feature transformation, which means that distribution alignment is often performed in the original feature space, where feature distortions are hard to overcome. On the other hand, subspace learning is not sufficient to reduce the distribution divergence. (2) unevaluated distribution alignment, which means that existing distribution alignment methods only align the marginal and conditional distributions with equal importance, while they fail to evaluate the different importance of these two distributions in real applications. In this paper, we propose a Manifold Embedded Distribution Alignment (MEDA) approach to address these challenges. MEDA learns a domain-invariant classifier in Grassmann manifold with structural risk minimization, while performing dynamic distribution alignment to quantitatively account for the relative importance of marginal and conditional distributions. To the best of our knowledge, MEDA is the first attempt to perform dynamic distribution alignment for manifold domain adaptation. Extensive experiments demonstrate that MEDA shows significant improvements in classification accuracy compared to state-of-the-art traditional and deep methods.

• The paper introduces MEDA, which leverages manifold feature learning and dynamic distribution alignment to resolve feature transformation and distribution misalignment issues.
• It employs Grassmann manifold embedding and geodesic flow kernel techniques to enhance cross-domain feature alignment and significantly reduce domain shift.
• Experimental results reveal a 3.5% improvement in average classification accuracy across benchmarks, outperforming leading traditional and deep learning approaches.

Visual Domain Adaptation with Manifold Embedded Distribution Alignment

The paper presents a method for addressing visual domain adaptation challenges through a novel approach named Manifold Embedded Distribution Alignment (MEDA). It proposes solutions to common issues in domain adaptation: degenerated feature transformation and unevaluated distribution alignment.

Main Contributions

The proposed MEDA method focuses on two primary advancements. Firstly, the method leverages manifold feature learning to counteract issues arising from feature distortions in the original space. Utilizing the Grassmann manifold, MEDA transforms features to better align cross-domain data geometrically. Secondly, it introduces dynamic distribution alignment, quantitatively evaluating the importance of aligning marginal and conditional distributions using an adaptive factor.

Technical Overview

MEDA's technical structure involves two key elements:

1. Manifold Feature Learning: It adaptively transforms feature space into the Grassmann manifold to capture geometrical structures, thereby reducing domain shift. Geodesic Flow Kernel (GFK) is employed to facilitate efficient feature transformation.
2. Dynamic Distribution Alignment: Unlike traditional methods that equally weigh marginal and conditional distributions, MEDA adjusts the focus dynamically based on the domains' distribution differences. This is achieved using a maximum mean discrepancy framework with an adaptive parameter that modulates the influence of each distribution type.

Experimental Results

The paper evaluates MEDA across several benchmark datasets, including Office+Caltech10, USPS+MNIST, ImageNet+VOC2007, and Office-31, using both traditional SURF and deep DeCaf6 features. MEDA consistently outperforms both state-of-the-art traditional and deep learning approaches, achieving a notable improvement of 3.5% in average classification accuracy across 28 tasks compared to the best baseline. This attests to its robustness and effectiveness in diverse domain adaptation scenarios.

Key Findings

• Dynamic Adaptation: MEDA's capacity to dynamically adapt distribution alignment is pivotal, as evidenced by varying optimal parameters across tasks.
• Manifold Learning: Manifold feature learning through the Grassmann manifold effectively counters feature distortion, enhancing feature alignment and ultimately prediction accuracy.
• Computational Efficiency: Despite its sophisticated mechanism, MEDA demonstrates comparable computational efficiency to leading baseline methods, making it viable for practical applications.

Implications and Future Directions

The insights and improvements presented in MEDA are crucial for advancing domain adaptation techniques. The quantitative evaluation of distribution importance not only enriches current methodologies but also sets a foundation for future transfer learning research. As domain adaptation remains a critical challenge, particularly in scenarios with limited labeled data or significantly differing source-target domains, MEDA's approach offers a pathway towards more adaptive and efficient solutions. Further research can build on MEDA's framework, exploring alternative manifolds or adaptive measures, and validating its applications across varied real-world domains.

This work substantially contributes to the transfer learning field, presenting a viable solution to long-standing challenges in domain adaptation.