Transformer-Based Source-Free Domain Adaptation (2105.14138v1)

Abstract: In this paper, we study the task of source-free domain adaptation (SFDA), where the source data are not available during target adaptation. Previous works on SFDA mainly focus on aligning the cross-domain distributions. However, they ignore the generalization ability of the pretrained source model, which largely influences the initial target outputs that are vital to the target adaptation stage. To address this, we make the interesting observation that the model accuracy is highly correlated with whether or not attention is focused on the objects in an image. To this end, we propose a generic and effective framework based on Transformer, named TransDA, for learning a generalized model for SFDA. Specifically, we apply the Transformer as the attention module and inject it into a convolutional network. By doing so, the model is encouraged to turn attention towards the object regions, which can effectively improve the model's generalization ability on the target domains. Moreover, a novel self-supervised knowledge distillation approach is proposed to adapt the Transformer with target pseudo-labels, thus further encouraging the network to focus on the object regions. Experiments on three domain adaptation tasks, including closed-set, partial-set, and open-set adaption, demonstrate that TransDA can greatly improve the adaptation accuracy and produce state-of-the-art results. The source code and trained models are available at https://github.com/ygjwd12345/TransDA.

• The paper introduces TransDA, a novel framework integrating a Transformer module and self-supervised knowledge distillation to achieve source-free domain adaptation without source data.
• The research demonstrates a strong correlation between improved model attention on objects using Transformers and increased prediction accuracy in target domains.
• TransDA achieves state-of-the-art accuracy on benchmarks like Office-Home, demonstrating a practical source-free domain adaptation solution without source data dependency.

Transformer-Based Source-Free Domain Adaptation: An Overview

The paper presents a novel approach to source-free domain adaptation (SFDA), offering insights into improving adaptation accuracy without relying on source domain data during the target adaptation phase. The research targets a critical gap in the domain adaptation literature by proposing to enhance the generalization ability of pretrained source models, thus optimizing the initial predictions vital for target adaptation. This is achieved through a framework called TransDA that incorporates a Transformer module to focus model attention on object regions, combined with a self-supervised knowledge distillation process utilizing pseudo-labels from the target domain.

Key Highlights

1. Framework Design: TransDA integrates Transformer-based attention mechanisms into a convolutional network, aiming to facilitate precise focus on object regions and enhance model generalization across unseen target domains. The Transformer module is positioned after the last convolutional layer of the ResNet-50, leveraging its aptitude for long-range dependencies to align model focus on pertinent image segments. The retention of the classifier and the evolution of the feature extractor are managed through the self-supervised knowledge distillation approach.
2. Attention Insight: The empirical paper draws a significant correlation between model accuracy and effective attention on objects using Grad-CAM visualizations. This observation underscores a novel perspective that improving the attention ability of models can substantially increase prediction accuracy in target domains.
3. Adaptation Strategies: The adaptation process combines information maximization, self-labeling with pseudo-labels, and self-knowledge distillation involving a teacher-student model architecture. These components collectively promote feature alignment and focus, crucially addressing the noise issues in pseudo-label generation, thereby enhancing the robustness of adaptation results.

Numerical Results and Implications

The paper's evaluation across three adaptation tasks (closed-set, partial-set, and open-set) on established benchmarks like Office-31, Office-Home, and VisDA demonstrates TransDA's superiority, marked by state-of-the-art accuracies. Specifically, notable improvements include an increase in closed-set adaptation accuracy on Office-Home from 71.8% to 79.3%, partial-set from 79.3% to 81.3%, and open-set from 72.8% to 76.8%.

Practical and Theoretical Implications

TransDA substantially contributes to SFDA by alleviating dependency on source data availability, which is pertinent amid data privacy concerns and resource constraints prevalent in real-world scenarios. The focus on enhancing model attention through Transformers opens avenues for further research into attention-based domain adaptation strategies, potentially extending to broader AI applications that require robust transfer learning mechanisms. The empirical insights offered can be foundational for designing future algorithms that prioritize attention quality over mere alignment of feature distributions.

Future Directions

The promising results and strategic insights encourage explorations into extending Transformer applications across varied domain adaptation settings and architectures. The intersection of attention mechanisms and domain adaptation offers fertile ground for advancements in AI-driven visual understanding and cross-domain generalization, serving diverse fields where data accessibility remains a prevailing challenge.

In conclusion, the paper presents a strong contribution to the SFDA field by proposing an innovative approach that tangibly improves adaptation accuracy. Through TransDA, the integration of attention mechanisms and knowledge distillation presents transformative avenues for enhancing the adaptability of pretrained models across domains without requiring source data, showcasing the potential for future developments in AI and machine learning research.