Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation Using Structurally Regularized...

Area of Science:

• Machine Learning
• Computer Vision
• Artificial Intelligence

Background:

• Unsupervised domain adaptation (UDA) aims to train models on labeled source data for unlabeled target data with differing distributions.
• Current UDA methods risk damaging target data structures, impacting generalization, especially in inductive settings.
• The assumption of structural similarity across domains is key to improving UDA.

Purpose of the Study:

• To propose a novel UDA method that directly uncovers intrinsic target discrimination without damaging data structures.
• To enhance model generalization in UDA tasks, particularly in inductive settings.
• To introduce a hybrid deep clustering framework for UDA.

Main Methods:

• Proposed H-SRDC (Hybrid Structurally Regularized Deep Clustering) integrating discriminative and generative clustering.
• Employed constrained clustering with structural source regularization based on domain similarity.
• Utilized a deep clustering framework minimizing KL-divergence, incorporating domain-shared classifiers and centroids.
• Extended H-SRDC for pixel-level UDA in semantic segmentation.

Main Results:

• H-SRDC outperforms existing methods on seven UDA benchmarks for image classification and semantic segmentation.
• Achieved superior performance in both inductive and transductive settings without explicit feature alignment.
• Demonstrated the effectiveness of structural similarity assumption and constrained clustering.

Conclusions:

• H-SRDC effectively addresses the generalization issue in UDA by preserving target data structures.
• The proposed hybrid deep clustering approach offers a robust solution for domain adaptation.
• The method shows significant promise for real-world applications requiring cross-domain generalization.