Domain-Symmetric Networks for Adversarial Domain Adaptation (1904.04663v2)

Abstract: Unsupervised domain adaptation aims to learn a model of classifier for unlabeled samples on the target domain, given training data of labeled samples on the source domain. Impressive progress is made recently by learning invariant features via domain-adversarial training of deep networks. In spite of the recent progress, domain adaptation is still limited in achieving the invariance of feature distributions at a finer category level. To this end, we propose in this paper a new domain adaptation method called Domain-Symmetric Networks (SymNets). The proposed SymNet is based on a symmetric design of source and target task classifiers, based on which we also construct an additional classifier that shares with them its layer neurons. To train the SymNet, we propose a novel adversarial learning objective whose key design is based on a two-level domain confusion scheme, where the category-level confusion loss improves over the domain-level one by driving the learning of intermediate network features to be invariant at the corresponding categories of the two domains. Both domain discrimination and domain confusion are implemented based on the constructed additional classifier. Since target samples are unlabeled, we also propose a scheme of cross-domain training to help learn the target classifier. Careful ablation studies show the efficacy of our proposed method. In particular, based on commonly used base networks, our SymNets achieve the new state of the art on three benchmark domain adaptation datasets.

• The paper presents Domain-Symmetric Networks, a two-level adversarial training framework that aligns both domain and category-level distributions to improve unsupervised adaptation.
• It employs a symmetric classifier design and cross-domain training to enhance feature invariance and effectively reduce distribution disparities.
• Experimental results on Office-31, ImageCLEF-DA, and Office-Home benchmarks highlight state-of-the-art accuracy gains over prior methods.

Insights on Domain-Symmetric Networks for Adversarial Domain Adaptation

The paper "Domain-Symmetric Networks for Adversarial Domain Adaptation" by Yabin Zhang, Hui Tang, Kui Jia, and Mingkui Tan presents a novel approach to unsupervised domain adaptation utilizing a framework they refer to as Domain-Symmetric Networks (SymNets). Unsupervised domain adaptation is a critical task in machine learning where the goal is to adapt models trained on a labeled source domain to achieve high performance on an unlabeled target domain. The challenge largely stems from the domain shift, i.e., the distribution discrepancy between the source and target domains.

Key Contributions

The authors introduce SymNets, which aim to align the joint distributions of feature and category across domains more effectively than prior approaches, primarily leveraging a two-level adversarial training scheme. The strategy is articulated around three core components:

1. Symmetric Design of Classifiers:
   • SymNets consist of a symmetric design involving separate task classifiers for the source and target domains, alongside an additional classifier that facilitates domain confusion and discrimination.
   • This design is proposed to explicitly consider the discrepancies not just at the domain level but also capturing finer-grained category-level nuances between source and target domains.
2. Two-Level Domain Confusion:
   • The innovation chiefly lies in formulating a two-level domain confusion loss focusing on both domain-level and category-level confusion.
   • The category-level confusion loss promotes invariance of intermediate features at corresponding categories across both domains, addressing a gap that prior methods exhibit by focusing largely on domain-level alignment.
3. Cross-Domain Training:
   • The approach employs a cross-domain training mechanism that incorporates labeled source samples to train target classifiers effectively, enhancing the discrimination of the resulting models without labeled target domain data.

Numerical Results and Efficacy

The efficacy of SymNets is demonstrated through comprehensive experiments on benchmark domain adaptation datasets: Office-31, ImageCLEF-DA, and Office-Home. The results indicate that SymNets achieve superior performance, establishing new state-of-the-art accuracy on these benchmarks.

• SymNets notably excel on challenging adaptation tasks, highlighting their ability to manage significant domain shifts effectively.
• The two-level adversarial training paradigm notably improves classification accuracy, confirming the theoretical benefits of aligning distributions at both category and domain levels.

Implications and Future Directions

The introduction of SymNets offers substantial insights into aligning domain and category-level distributions in domain adaptation. This alignment could effectively reduce the error in tasks where domain shift presents a significant bottleneck.

From a theoretical standpoint, the methodology suggests a broader interpretation of adversarial training frameworks to encompass not only domain-level features but also intrinsic class structure alignment. Practically, this advancement holds the potential to enhance cross-domain adaptability in varied applications, particularly where vast annotated datasets are not feasible.

Future research directions may explore extending the SymNet framework to more complex tasks or larger domains. Investigating deeper architectures or leveraging additional domain-specific knowledge might further push the boundaries of transferability and adaptation.

In summary, this paper makes a compelling contribution to the domain adaptation toolbox, proposing methodologies that proficiently address domain and category-level discrepancies in feature spaces, augmented by robust empirical validations on standardized benchmarks.