Gradually Vanishing Bridge for Adversarial Domain Adaptation (2003.13183v1)

Abstract: In unsupervised domain adaptation, rich domain-specific characteristics bring great challenge to learn domain-invariant representations. However, domain discrepancy is considered to be directly minimized in existing solutions, which is difficult to achieve in practice. Some methods alleviate the difficulty by explicitly modeling domain-invariant and domain-specific parts in the representations, but the adverse influence of the explicit construction lies in the residual domain-specific characteristics in the constructed domain-invariant representations. In this paper, we equip adversarial domain adaptation with Gradually Vanishing Bridge (GVB) mechanism on both generator and discriminator. On the generator, GVB could not only reduce the overall transfer difficulty, but also reduce the influence of the residual domain-specific characteristics in domain-invariant representations. On the discriminator, GVB contributes to enhance the discriminating ability, and balance the adversarial training process. Experiments on three challenging datasets show that our GVB methods outperform strong competitors, and cooperate well with other adversarial methods. The code is available at https://github.com/cuishuhao/GVB.

• The paper introduces a novel Gradually Vanishing Bridge mechanism that reduces domain-specific artifacts in both generator and discriminator components.
• The methodology leverages dual bridge systems (GVB-G and GVB-D) to facilitate robust learning of domain-invariant representations.
• Experimental results demonstrate significant improvements, achieving 89.3% accuracy on Office-31 and promising performance across multiple datasets.

Gradually Vanishing Bridge for Adversarial Domain Adaptation

The paper "Gradually Vanishing Bridge for Adversarial Domain Adaptation" introduces a novel framework designed to improve unsupervised domain adaptation (UDA) through the incorporation of a Gradually Vanishing Bridge (GVB) mechanism on both the generator and discriminator components of adversarial frameworks. The primary objective is to facilitate effective domain-invariant representation learning while mitigating domain-specific characteristics that impede cross-domain knowledge transfer.

Context and Motivation

In the field of unsupervised domain adaptation, the challenge predominantly lies in addressing domain discrepancies caused by rich domain-specific characteristics. Existing solutions typically tackle this challenge by minimizing domain discrepancies directly, often leading to suboptimal outcomes due to residual domain-specific features that persist in supposedly invariant representations. Methods leveraging generative adversarial networks (GANs) have gained traction, wherein domain adaptation is achieved by playing a minmax game between a generator that aims to confuse a discriminator tasked with distinguishing between source and target domains.

Contribution and Methodology

The authors introduce the concept of a "bridge" to model domain-specific components, thus creating an intermediate domain that theoretically exhibits fewer domain-specific artifacts. The innovation is twofold:

1. GVB on Generator (GVB-G): This mechanism models domain-specific aspects and connects original domains to the intermediate domain, effectively reducing the range of adverse domain characteristics over the adaptation process. By minimizing the influence range of GVB, the generator learns more robust domain-invariant representations.
2. GVB on Discriminator (GVB-D): This introduces a supplementary discriminative capability that aids in maintaining balance during adversarial training. It ensures that the discriminator is effectively focused on distinguishing domain properties without being overwhelmed by adversarial signals.

The combined GVB-GD framework integrates both mechanisms to maintain equilibrium in the adversarial learning process, exploiting their symbiotic relationship to achieve superior performance.

Results and Implications

Experiments conducted on three challenging datasets—Office-31, Office-Home, and VisDA-2017—demonstrate that the GVB frameworks exceed the performance of several contemporary adversarial and non-adversarial UDA methods. For instance, the method achieves an accuracy of 89.3% on the Office-31 dataset, marking a significant advancement in domain adaptation performance. Furthermore, GVB-enhanced methods show robust applicability across small and large datasets with varying domain discrepancies.

Theoretical and Practical Implications

By effectively integrating the GVB mechanism, the paper contributes to theoretical advancements in adversarial domain adaptation by presenting a structured approach to mitigating residual domain-specific characteristics. The emphasis on gradual adaptation further aligns with understanding dynamic knowledge transfer more comprehensively. Practically, the method holds promise for improving AI models deployed in dynamically shifting environments, such as autonomous driving and cross-site healthcare diagnostics, where the receipt of unlabeled domain-shifted data is common.

Future Directions

Looking ahead, future research may explore the application of this framework in other domains beyond computer vision, such as natural language processing and audio signal processing. Furthermore, the exploration of more sophisticated architectures for bridge modeling or varying bridge functions could enhance the granularity and efficiency of domain adaptation processes. Examining semi-supervised domains or cases involving more complex, multi-domain transfer scenarios also offers fertile ground for extending these insights.

By designing a mechanism to progressively reduce domain-specific artifacts while improving domain invariance, the authors have presented a compelling methodological advancement in the domain adaptation paradigm, setting the groundwork for future explorations in robust cross-domain transfer learning.