Associative Domain Adaptation (1708.00938v1)

Abstract: We propose associative domain adaptation, a novel technique for end-to-end domain adaptation with neural networks, the task of inferring class labels for an unlabeled target domain based on the statistical properties of a labeled source domain. Our training scheme follows the paradigm that in order to effectively derive class labels for the target domain, a network should produce statistically domain invariant embeddings, while minimizing the classification error on the labeled source domain. We accomplish this by reinforcing associations between source and target data directly in embedding space. Our method can easily be added to any existing classification network with no structural and almost no computational overhead. We demonstrate the effectiveness of our approach on various benchmarks and achieve state-of-the-art results across the board with a generic convolutional neural network architecture not specifically tuned to the respective tasks. Finally, we show that the proposed association loss produces embeddings that are more effective for domain adaptation compared to methods employing maximum mean discrepancy as a similarity measure in embedding space.

• The paper introduces Associative Domain Adaptation, a method using a novel associative loss to learn domain-invariant embeddings by aligning statistical associations based on source labels.
• The proposed method achieves state-of-the-art performance on various standard domain adaptation benchmarks, outperforming previous methods like MMD, CORAL, and DANN.
• Associative Domain Adaptation reduces the need for labeled target data by preserving class-specific information when transferring knowledge across different data domains.

Associative Domain Adaptation: An Overview

The paper "Associative Domain Adaptation" presents a novel technique for domain adaptation in deep learning, leveraging neural networks to infer class labels in an unlabeled target domain by utilizing the statistical properties of a labeled source domain. The primary contribution of this work lies in the introduction of an associative loss, designed to create domain-invariant embeddings while minimizing classification error on the source domain. This approach is distinct from existing methods such as maximum mean discrepancy (MMD) which primarily address domain alignment without leveraging class labels.

Methodology

The authors propose a two-part loss structure comprising:

1. Classification Loss: A traditional supervised learning component that encourages discrimination by minimizing prediction error on the source data.
2. Association Loss: This novel loss enforces statistical similarity between source and target embeddings based on associations formed in embedding space as described by Haeusser et al. This loss leverages source label information to ensure that associations are class discriminative, avoiding unwanted blend of class identities across domains.

The integration of these two loss components allows for a balance between reducing domain shift via embedding similarity and maintaining class discrimination, which are inherently opposing objectives in domain adaptation.

Performance Evaluation

The paper assesses the effectiveness of the proposed associative domain adaptation method against several benchmarks: MNIST to MNIST-M, Synthetic Digits to SVHN, SVHN to MNIST, and Synthetic Signs to GTSRB. In each task, the method achieves state-of-the-art performance, demonstrating superior target domain accuracy compared with existing methods like CORAL and DANN. Notably, the paper highlights the efficacy of associative learning over MMD, citing improved classification accuracy on target domains.

Implications

The practical implications of this work are significant, especially in fields where labeled data in the target domain is scarce or expensive to acquire. By effectively utilizing domain adaptation, models can be trained with less reliance on labeled data from the target domain, facilitating the deployment of machine learning applications in real-world scenarios with varying data distributions.

Theoretically, the paper provides an alternative perspective to domain adaptation by emphasizing the role of class-specific associations in achieving domain invariance. The association mechanism inherently respects class boundaries, which is a distinct advantage over broader domain alignment measures that may overlook class specificity.

Future Directions

Future research could explore extending associative domain adaptation to more complex domain shifts, multi-source domain adaptation, or incremental learning scenarios. Additionally, further investigation into the scalability and optimization of associative losses in larger, more varied datasets could enhance its applicability.

In sum, the "Associative Domain Adaptation" paper introduces a valuable approach to domain adaptation with neural networks, characterized by its focus on obtaining class-specific domain invariant embeddings. This method holds promise for improving the generalization of models across different domains, addressing a critical challenge in machine learning with substantial theoretical and practical implications.