Adversarial Domain Adaptation with Domain Mixup (1912.01805v1)

Abstract: Recent works on domain adaptation reveal the effectiveness of adversarial learning on filling the discrepancy between source and target domains. However, two common limitations exist in current adversarial-learning-based methods. First, samples from two domains alone are not sufficient to ensure domain-invariance at most part of latent space. Second, the domain discriminator involved in these methods can only judge real or fake with the guidance of hard label, while it is more reasonable to use soft scores to evaluate the generated images or features, i.e., to fully utilize the inter-domain information. In this paper, we present adversarial domain adaptation with domain mixup (DM-ADA), which guarantees domain-invariance in a more continuous latent space and guides the domain discriminator in judging samples' difference relative to source and target domains. Domain mixup is jointly conducted on pixel and feature level to improve the robustness of models. Extensive experiments prove that the proposed approach can achieve superior performance on tasks with various degrees of domain shift and data complexity.

Adversarial Domain Adaptation with Domain Mixup: A Critical Review

The paper "Adversarial Domain Adaptation with Domain Mixup" seeks to advance unsupervised domain adaptation methodologies by addressing challenges inherent in current adversarial learning-based adaptation methods. Specifically, the authors identify two limitations in existing domain adaptation methods: the inadequacy of source and target samples alone to ensure domain invariance in latent spaces, and the binary nature of domain discriminators that fail to explore inter-domain subtleties comprehensively. The presented solution, Domain Mixup in Adversarial Domain Adaptation (DM-ADA), is developed to tackle these challenges effectively.

The primary innovation of DM-ADA lies in its strategy to integrate domain mixup at both the pixel and feature levels, thereby establishing a more continuous latent space. This approach enhances the robustness of models against domain shifts by incorporating soft labels and triplet loss, allowing greater exploration of intrinsic sample structures and inter-domain information.

Methodology Overview

The DM-ADA framework extends variational autoencoders (VAE) from the generative adversarial network (GAN) family, enabling simultaneous training on classification tasks while generating auxiliary source-like images from learned embeddings. A two-fold domain mixup is applied in this setting:

1. Pixel-level Mixup: Source and target domain images are linearly interpolated, creating a continuous representation of domain samples. This procedure helps bridge gaps between domains and refines discriminator predictions with soft domain labels.
2. Feature-level Mixup: Analogously, the feature representations of domains undergo linear mixing, promoting a seamless latent code distribution that is invariant to domain shifts. This continuous embedding space aims to mitigate classification errors due to distribution oscillations.

Further, the domain discriminator is guided by a mixup-based soft label strategy and a triplet loss function that orchestrates a careful alignment between two domains. The triplet loss encourages the discriminator to capture a sample's relative differences with respect to its source and target domains.

Experimental Evaluation

The efficacy of DM-ADA is validated through rigorous testing on three prominent domain adaptation benchmarks: digits datasets, Office-31, and the VisDA-2017 challenge. Within these tasks, DM-ADA consistently outperforms state-of-the-art methods. For example, on the challenging digits dataset scenarios (e.g., SVHN to MNIST), DM-ADA achieved superior accuracy, illustrating its robustness in managing significant domain shifts.

In Office-31 tasks, often characterized by fewer samples per class, DM-ADA's pixel-level and feature-level mixup strategies effectively improved feature augmentations, producing top-tier accuracy. Meanwhile, in the VisDA-2017 domain adaptation challenge, known for significant visual discrepancies between synthetic and real domains, DM-ADA again illustrated its comparative advantage, underscoring the role of robust intermediate status modeling in domain adaptation.

Implications and Future Directions

The paper demonstrates that by systematically leveraging inter-domain representation through mixup strategies, both in feature and pixel space, domain adaptation can achieve more effective domain alignment. This methodological development poses implications for real-world scenarios where such adaptability between synthetic and real-world data could enable improved model generalization without the need for extensive labeled datasets.

Future developments could explore:

• Applying DM-ADA to larger and more diverse datasets to investigate scalability and adaptability to even greater domain shifts.
• Extension of domain mixup strategies to multi-modal data, which often require additional nuanced understanding of cross-modal mappings.
• Integration with semi-supervised techniques to exploit minimal labeled data when available, bridging the gap between fully supervised learning and unsupervised domain adaptation.

The proposed DM-ADA framework effectively harnesses mixup principles within domain adaptation, signifying a promising advancement in the pursuit of robust and flexible domains adapted across varied and complex tasks.