Deep Subdomain Adaptation Network for Image Classification (2106.09388v1)

Abstract: For a target task where labeled data is unavailable, domain adaptation can transfer a learner from a different source domain. Previous deep domain adaptation methods mainly learn a global domain shift, i.e., align the global source and target distributions without considering the relationships between two subdomains within the same category of different domains, leading to unsatisfying transfer learning performance without capturing the fine-grained information. Recently, more and more researchers pay attention to Subdomain Adaptation which focuses on accurately aligning the distributions of the relevant subdomains. However, most of them are adversarial methods which contain several loss functions and converge slowly. Based on this, we present Deep Subdomain Adaptation Network (DSAN) which learns a transfer network by aligning the relevant subdomain distributions of domain-specific layer activations across different domains based on a local maximum mean discrepancy (LMMD). Our DSAN is very simple but effective which does not need adversarial training and converges fast. The adaptation can be achieved easily with most feed-forward network models by extending them with LMMD loss, which can be trained efficiently via back-propagation. Experiments demonstrate that DSAN can achieve remarkable results on both object recognition tasks and digit classification tasks. Our code will be available at: https://github.com/easezyc/deep-transfer-learning

• The paper introduces a novel DSAN that aligns local subdomain features using LMMD, bypassing adversarial training for efficient improvements.
• The paper demonstrates superior performance with an accuracy of 88.4% on Office-31 and strong results on multiple datasets, highlighting its scalability.
• The paper offers theoretical insights and practical implications for extending subdomain adaptation to complex tasks, paving the way for future research.

Deep Subdomain Adaptation Network for Image Classification

The paper presents a novel approach to image classification in the context of domain adaptation, specifically addressing the challenge of fine-grained adaptation between subdomains. Traditional domain adaptation methods predominantly focus on aligning global distributions between source and target domains. However, this strategy can overlook vital subdomain-specific nuances, which is where the Deep Subdomain Adaptation Network (DSAN) distinguishes itself.

Key Contributions

1. Local Maximum Mean Discrepancy (LMMD): DSAN introduces LMMD as a mechanism for aligning local subdomain distributions, rather than relying solely on global distribution matching. This approach leverages domain-specific layer activations and effectively incorporates a non-adversarial mechanism to align distributions without the complexity of adversarial training.
2. Simple and Efficient Design: The proposed method eschews adversarial training, which is common in other subdomain adaptation techniques. This results in faster convergence and implementation simplicity while achieving notable performance gains.
3. Performance Evaluation: The paper provides extensive experimental validation of DSAN on several datasets, including ImageCLEF-DA, Office-31, Office-Home, VisDA-2017, and Adaptiope. DSAN consistently demonstrates superior performance compared to both global domain adaptation methods and other subdomain adaptation methods, often improving classification accuracy by significant margins.
4. Theoretical Insights: The paper utilizes domain adaptation theory to argue for the advantage of subdomain alignment, showing that realigning subdomains effectively reduces both global and local distribution discrepancies.

Numerical Results

DSAN achieves remarkable results across various datasets. For instance, on the Office-31 dataset, DSAN achieves an average accuracy of 88.4%, outperforming several well-recognized methods like CDAN and MADA. On more challenging datasets like VisDA-2017, DSAN continues to show its efficacy with strong classification results, underscoring its capability to handle more realistic and diverse domain shifts.

Implications and Future Directions

The approach suggested by DSAN has practical implications: it can be integrated seamlessly into existing network architectures, thus making it suitable for a wide range of applications where domain adaptation is critical. The focus on subdomain adaptation may inspire further research into even more granular subdomain discriminative features, potentially leading to advancements in domains like medical imaging or autonomous driving where fine-grained distinctions are crucial.

Theoretically, the success of DSAN without adversarial training suggests potential for exploring further non-adversarial methods in the domain adaptation landscape. Future work could investigate extending this approach to more complex tasks beyond image classification, such as object detection or segmentation, and explore the integration of subdomain adaptation in sequence-based models.

DSAN's potential for improving domain adaptation processes marks a significant contribution to the field of machine learning and AI, aiding in the pursuit of models that generalize effectively across disparate domains without extensive labeled data. This work not only advances image classification but also sets the stage for bridging subdomain-specific gaps in various data-driven applications.