Partial Adversarial Domain Adaptation (1808.04205v1)

Abstract: Domain adversarial learning aligns the feature distributions across the source and target domains in a two-player minimax game. Existing domain adversarial networks generally assume identical label space across different domains. In the presence of big data, there is strong motivation of transferring deep models from existing big domains to unknown small domains. This paper introduces partial domain adaptation as a new domain adaptation scenario, which relaxes the fully shared label space assumption to that the source label space subsumes the target label space. Previous methods typically match the whole source domain to the target domain, which are vulnerable to negative transfer for the partial domain adaptation problem due to the large mismatch between label spaces. We present Partial Adversarial Domain Adaptation (PADA), which simultaneously alleviates negative transfer by down-weighing the data of outlier source classes for training both source classifier and domain adversary, and promotes positive transfer by matching the feature distributions in the shared label space. Experiments show that PADA exceeds state-of-the-art results for partial domain adaptation tasks on several datasets.

• The paper introduces PADA, which minimizes negative transfer by selectively down-weighing irrelevant source classes in partial domain adaptation.
• It employs a novel weighting mechanism that focuses on the shared label space between domains to ensure effective and noise-reduced transfer learning.
• Extensive experiments on datasets like Office-31 and VisDA2017 show that PADA outperforms traditional methods such as DANN and DAN in mismatch scenarios.

Partial Adversarial Domain Adaptation: A Detailed Exploration

The paper "Partial Adversarial Domain Adaptation" presents an innovative approach to domain adaptation when the source and target domains do not fully overlap in terms of label space. Traditional domain adversarial learning frameworks assume identical label spaces, which often do not hold in practical scenarios. The authors address this gap by proposing a partial domain adaptation method that relaxes the ubiquitous requirement of a fully shared label space, enabling transfer learning between more complex domain relationships where the target label space is a subset of the source label space.

Main Contributions

The paper introduces Partial Adversarial Domain Adaptation (PADA), which is designed to minimize the detrimental effects of negative transfer that occur due to disjoint label spaces. This is achieved by selectively down-weighing the contribution of outlier source classes that do not align with the target domain's label space. The proposed approach focuses on the intersection of both domains' classes, enhancing positive transfer while reducing noise from irrelevant data.

Key contributions of the paper include:

1. Introduction of Partial Domain Adaptation: The paper acknowledges the practical challenge where target domains often have fewer labels than source domains, leading to the novel scenario of partial domain adaptation.
2. Framework for PADA: The authors develop a formal framework that effectively filters out irrelevant source domain classes by using a weighting mechanism based on the outputs of a source classifier applied to target data. This allows the model to selectively focus on applicable source data.
3. Empirical Validation: Extensive experiments across multiple datasets, such as Office-31, Office-Home, ImageNet-Caltech, and VisDA2017, demonstrate that PADA consistently outperforms state-of-the-art methods by effectively addressing label mismatch issues.

Empirical Results

The experimental results showcase the robustness of PADA. The approach yields superior performance compared to baseline models like Domain Adversarial Neural Network (DANN) and Deep Adaptation Network (DAN), which struggled due to their naïve alignment of disparate label spaces. Notably, PADA excels in settings where the source domain has many more classes than the target domain, confirming its ability to down-weigh irrelevant source data effectively. Intriguingly, the approach maintains competitive performance even in standard domain adaptation scenarios, indicating its versatile applicability.

Theoretical and Practical Implications

Theoretically, PADA provides a significant advancement in domain adaptation research by challenging the conventional full label space assumption. It underscores the importance of selectively weighing data in adversarial environments, opening new avenues for future exploration in partial transfer learning settings.

Practically, the implications for various applications are profound. As big data from diverse sources become increasingly common, PADA can facilitate more effective and efficient adaptation of models across differing label spaces. This capability is crucial for deploying AI solutions where obtaining labeled data is resource-intensive or even infeasible for certain domains.

Speculations on Future Directions

Given the promising results and foundational advances offered by PADA, several future research directions are conceivable:

• Extended Scenarios: Investigate the efficacy of PADA in broader semi-supervised and unsupervised settings where even partial labels from the source domain may not be reliable.
• Scalability: Enhance the efficiency of the approach for extremely large datasets and investigate its integration with other scalable deep learning architectures.
• Complex Relationships: Explore the potential of PADA in multi-source and multi-target scenarios where domain relationships are even more intricate.

Conclusion

The paper's contribution represents a pivotal shift in addressing partial domain adaptation challenges. By effectively managing the source-target domain label discrepancies through an innovative partial adversarial framework, the research empowers a host of applications requiring nuanced and flexible domain adaptation. The scholarly community would benefit from further exploration and validation of these concepts across diverse problem domains.