Domain Agnostic Learning with Disentangled Representations (1904.12347v1)

Abstract: Unsupervised model transfer has the potential to greatly improve the generalizability of deep models to novel domains. Yet the current literature assumes that the separation of target data into distinct domains is known as a priori. In this paper, we propose the task of Domain-Agnostic Learning (DAL): How to transfer knowledge from a labeled source domain to unlabeled data from arbitrary target domains? To tackle this problem, we devise a novel Deep Adversarial Disentangled Autoencoder (DADA) capable of disentangling domain-specific features from class identity. We demonstrate experimentally that when the target domain labels are unknown, DADA leads to state-of-the-art performance on several image classification datasets.

• The paper introduces DADA, which outperforms baseline models by disentangling domain-specific from class-related features for improved knowledge transfer.
• It employs adversarial training, mutual information minimization, and ring-style normalization to optimize feature separation.
• Experimental results demonstrate state-of-the-art performance on datasets like Digit-Five and DomainNet, confirming robust cross-domain adaptation.

Domain Agnostic Learning with Disentangled Representations

The paper "Domain Agnostic Learning with Disentangled Representations" introduces a novel approach to the task of domain-agnostic learning (DAL) - a process designed to transfer knowledge from a labeled source domain to multiple unlabeled and varied target domains. This paper proposes a strategic advancement over previously established methods in unsupervised domain adaptation (UDA) by addressing the limitation wherein the identification of target domain labels is a prior requirement, which is unrealistic in many real-world scenarios.

Key Contributions

The central contribution of this paper is the development of a novel architecture called Deep Adversarial Disentangled Autoencoder (DADA). The main capabilities of DADA include the ability to disentangle domain-specific features from class identities, facilitating more effective knowledge transfer across various domains. This paper's unique approach is evaluated against traditional models, demonstrating that DADA achieves state-of-the-art performance across several prominent image classification datasets like Digit-Five, Office-Caltech10, and DomainNet.

Technical Approach

DADA comprises multiple components working synergistically:

1. Class Disentanglement: It employs class disentanglement to filter out class-irrelevant features, thereby reducing noise and enhancing the useful information extracted from the source domain data.
2. Domain Disentanglement: The architecture utilizes domain disentanglement for separating domain-invariant features from domain-specific features. This separation is executed through adversarial training using a domain identifier, which aligns source domain features with those of heterogeneous target domains.
3. Mutual Information Minimization: To further enhance the feature disentanglement, DADA incorporates a mutual information minimizer to ensure domain-invariant, domain-specific, and class-irrelevant features are optimally separated.
4. Ring-style Normalization: This normalization technique integrates with a Geman-McClure model to maintain balanced angular classification margins, ensuring embedded features are well standardized, an advantage noted in heterogeneous datasets.

Experimental Results

Empirical analysis across multiple datasets illustrates significant improvements in task performance using DADA over other baseline models like DAN, DANN, and MCD. For instance, on the "Digit-Five" dataset tasks, DADA outperforms conventional methods, yielding substantial accuracy improvements with marked benefits noted when domain and class features are robustly disentangled. The authors provide a comprehensive quantitative and qualitative analysis, including t-SNE visualizations and $\mathcal{A}$-Dist of learned features, substantiating the effectiveness of their approach.

Implications and Future Work

This research opens avenues for developing more robust deep learning models that can adapt across various domains without explicit domain labels, a capability crucial for real-world applications such as web image classification and handwritten character recognition in varied scenarios. Further theoretical examination could extend to optimizing disentanglement procedures, reinforcing domain-invariance, and exploiting advanced disentanglement metrics.

Conclusion

The paper makes a compelling case for domain-agnostic learning by effectively implementing advanced disentanglement strategies. By innovatively addressing domain shifts without relying on predefined domain distinctions, DADA presents a practical, scalable solution for cross-domain generalization tasks. This work sets a meaningful precedent for future exploration in domain adaptation and transfer learning paradigms.