Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation (1808.09347v2)

Abstract: Recently, considerable effort has been devoted to deep domain adaptation in computer vision and machine learning communities. However, most of existing work only concentrates on learning shared feature representation by minimizing the distribution discrepancy across different domains. Due to the fact that all the domain alignment approaches can only reduce, but not remove the domain shift. Target domain samples distributed near the edge of the clusters, or far from their corresponding class centers are easily to be misclassified by the hyperplane learned from the source domain. To alleviate this issue, we propose to joint domain alignment and discriminative feature learning, which could benefit both domain alignment and final classification. Specifically, an instance-based discriminative feature learning method and a center-based discriminative feature learning method are proposed, both of which guarantee the domain invariant features with better intra-class compactness and inter-class separability. Extensive experiments show that learning the discriminative features in the shared feature space can significantly boost the performance of deep domain adaptation methods.

• The paper integrates domain alignment with discriminative feature learning to enhance intra-class compactness and inter-class separability.
• It employs a two-stream CNN architecture with CORAL to align feature covariances and mitigate misclassification under domain shifts.
• Empirical results on benchmarks like Office-31 show significant improvements in target classification accuracy over state-of-the-art methods.

Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation

The paper by Chao Chen et al. presents a novel approach to enhance the effectiveness of deep domain adaptation techniques by integrating domain alignment with discriminative feature learning. This work addresses a significant limitation in current domain adaptation methods, which focus primarily on reducing domain discrepancies without ensuring that features are discriminative. This oversight can result in target samples being misclassified due to their distribution near cluster edges or distance from class centers.

Core Contributions

1. Integration of Discriminative Feature Learning: The paper introduces two strategies for discriminative feature learning: Instance-Based and Center-Based methods. Both strategies aim to improve intra-class compactness and inter-class separability, ensuring more robust domain-invariant features.
2. Joint Domain Alignment Approach: By combining domain alignment with discriminative feature learning, the method not only reduces domain shift but also inherently supports better classification outcomes.
3. Empirical Validation: The proposed Joint Domain Alignment and Discriminative Feature Learning (JDDA) strategy demonstrates significant performance improvements over state-of-the-art methods on popular benchmarks such as the Office-31 dataset and a large-scale digital recognition dataset.

Methodology

The JDDA approach leverages a two-stream Convolutional Neural Network (CNN) architecture where one stream processes source data and the other handles target data. The authors employ Correlation Alignment (CORAL) to align domain-specific feature covariances, a process which is bolstered by the discriminative loss functions that encourage closer intra-class features and more distinct inter-class features.

Instance-Based Discriminative Loss

This loss function motivates intra-class features to be closer than a margin, while inter-class features are spaced further apart than another margin. This instance-level approach contributes to reducing the feature overlap between classes within the shared latent space.

Center-Based Discriminative Loss

In contrast, this loss function centers around aligning features to their respective class centroid, thereby enhancing feature cluster separability and compactness without incurring the computational cost of pairwise instance distances. The authors highlight that this method converges faster due to its reliance on cohort-style updates towards "global class centers."

Experimental Findings

The JDDA method not only outperformed stand-alone domain alignment techniques but also achieved superior results compared to adversarial adaptation strategies in numerous domain adaptation tasks. Particularly notable is the performance on challenging adaptations involving significant domain shifts, such as transferring from SVHN to MNIST. The results show that JDDA considerably enhances target domain classification accuracy, especially in tasks characterized by limited or unsupervised target labels.

Implications and Speculations

The integration of discriminative feature learning within the domain adaptation framework opens new avenues for improving cross-domain classification tasks. The substantial improvements demonstrated in this work suggest that future research should further investigate synergetic approaches that enhance feature discrimination alongside domain alignment. This blend may conceivably extend to other machine learning models beyond CNNs and image classification, promoting better generalization across varied datasets and application domains.

Thus, the JDDA approach by Chen et al. sets a new precedent for incorporating feature space discrimination into the domain adaptation paradigm, potentially catalyzing advancements in unsupervised learning and adaptation techniques. Future directions may explore extending and refining these techniques for more complex and nuanced adaptation scenarios in AI and beyond.