Unsupervised Domain Adaptation using Regularized Hyper-graph Matching
This addresses the problem of distribution shift between training and testing data for image classification, but it is incremental as it builds on existing graph-matching techniques.
The paper tackles unsupervised domain adaptation for image classification by proposing a method that matches source and target domain samples using regularized hyper-graph matching with first- to third-order similarities, achieving improved performance over state-of-the-art approaches on standard datasets.
Domain adaptation (DA) addresses the real-world image classification problem of discrepancy between training (source) and testing (target) data distributions. We propose an unsupervised DA method that considers the presence of only unlabelled data in the target domain. Our approach centers on finding matches between samples of the source and target domains. The matches are obtained by treating the source and target domains as hyper-graphs and carrying out a class-regularized hyper-graph matching using first-, second- and third-order similarities between the graphs. We have also developed a computationally efficient algorithm by initially selecting a subset of the samples to construct a graph and then developing a customized optimization routine for graph-matching based on Conditional Gradient and Alternating Direction Multiplier Method. This allows the proposed method to be used widely. We also performed a set of experiments on standard object recognition datasets to validate the effectiveness of our framework over state-of-the-art approaches.