Unified Deep Supervised Domain Adaptation and Generalization (1709.10190v1)

Abstract: This work provides a unified framework for addressing the problem of visual supervised domain adaptation and generalization with deep models. The main idea is to exploit the Siamese architecture to learn an embedding subspace that is discriminative, and where mapped visual domains are semantically aligned and yet maximally separated. The supervised setting becomes attractive especially when only few target data samples need to be labeled. In this scenario, alignment and separation of semantic probability distributions is difficult because of the lack of data. We found that by reverting to point-wise surrogates of distribution distances and similarities provides an effective solution. In addition, the approach has a high speed of adaptation, which requires an extremely low number of labeled target training samples, even one per category can be effective. The approach is extended to domain generalization. For both applications the experiments show very promising results.

• The paper presents a unified framework using a Siamese network to align visual domains via a novel CCSA loss for robust performance.
• It introduces point-wise surrogates to compute distribution distances effectively even with very few labeled target samples.
• Experimental results on benchmarks like Office and MNIST-USPS demonstrate significant accuracy improvements over previous methods.

Unified Deep Supervised Domain Adaptation and Generalization

The paper "Unified Deep Supervised Domain Adaptation and Generalization" by Saeid Motiian et al., introduces a unified framework for addressing the challenge of visual supervised domain adaptation (SDA) and domain generalization (DG) using deep models. The work capitalizes on the Siamese network architecture to learn an embedding space where visual domains are semantically aligned yet distinct. This framework is particularly advantageous in scenarios where only a few labeled samples from the target domain are available, ensuring efficient adaptation and generalization with minimal data.

Key Contributions

The primary contributions of the paper are:

1. Unified Framework for SDA and DG: The authors present a cohesive methodology that leverages the Siamese network architecture to address both SDA and DG by learning a discriminative embedding space.
2. Point-wise Surrogates for Distribution Distances: To tackle the limited size of target domain samples, the authors propose the use of point-wise surrogates. This approach enables effective computation of distribution distances and similarities even with minimal data.
3. CCSA Loss: The introduction of the Classification and Contrastive Semantic Alignment (CCSA) loss, which combines classification loss, semantic alignment loss, and class separation loss, ensures both high classification performance and robust domain adaptation.

Experimental Results

The experiments are conducted on several benchmark datasets, including the Office dataset, MNIST, USPS, and the VLCS dataset. The results demonstrate the efficacy of the proposed method in both SDA and DG tasks, highlighting its superiority over existing approaches.

Office Dataset

The Office dataset, a standard benchmark for domain adaptation, encompasses 31 object categories across three domains: Amazon, DSLR, and Webcam. The proposed CCSA method significantly improves classification accuracy, especially in scenarios with substantial domain shifts. For instance, the accuracy in the $\mathcal{A} \rightarrow \mathcal{W}$ task increased to 88.2%, compared to 82.7% of the previous state-of-the-art.

MNIST-USPS

For the MNIST and USPS digit datasets, even with just one labeled target sample per category, the CCSA framework achieved an average accuracy of 81.7%, which further improved as the number of labeled samples increased, reaching up to 91.4% with eight samples per category.

VLCS Dataset

In domain generalization tasks involving the VLCS dataset, the CCSA method outperformed existing DG approaches. For example, in the task $$\mathcal{C},\mathcal{S} \rightarrow \mathcal{V},\mathcal{L}$$, the proposed method achieved an accuracy of 60.2%, illustrating its capability to handle domain shifts effectively.

Implications and Future Work

The research offers substantial practical and theoretical implications. Practically, it addresses the pressing need for efficient domain adaptation and generalization methods in computer vision applications where labeled target data is scarce. Theoretically, it provides a robust framework that can be extended to various deep learning models and tasks.

The methodology's ability to generalize across domains with limited labeled data opens doors for future developments in real-time adaptation tasks, such as robotics and autonomous systems, where rapid and efficient learning from minimal data is crucial. Future work could explore the integration of more sophisticated distance metrics and similarity measures to enhance the CCSA framework further.

In summary, the paper by Motiian et al. provides a significant advancement in the field of domain adaptation and generalization, offering a versatile and efficient approach for handling visual recognition tasks in the face of domain shifts and limited labeled data. This work paves the way for further research and development in adaptive learning systems.