Unsupervised domain adaptation using feature aligned maximum classifier discrepancy

Prasanna Reddy Pulakurthi; Sohail A. Dianat; Majid Rabbani; Suya You; Raghuveer M. Rao

doi:10.1117/12.2646422

3 October 2022 Unsupervised domain adaptation using feature aligned maximum classifier discrepancy

Prasanna Reddy Pulakurthi, Sohail A. Dianat, Majid Rabbani, Suya You, Raghuveer M. Rao

Proceedings Volume 12227, Applications of Machine Learning 2022; 1222707 (2022) https://doi.org/10.1117/12.2646422
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

The maximum classifier discrepancy method has achieved great success in solving unsupervised domain adaptation tasks for image classification in recent years. Its basic structure consists of a feature generator and two classifiers that aim to maximize the classifier discrepancy while minimizing the generator discrepancy of the target samples. This method improves the performance of the existing adversarial training methods by employing task-specific classifiers that remove the ambiguity in classifying the target samples near the class boundaries. In this paper, we propose a modified network architecture and two training objectives to further boost the performance of the maximum classifier discrepancy method. The first training objective minimizes the feature level discrepancy and forces the generator to generate domain invariant features. This training objective is particularly beneficial when the source and the target domain distributions are vastly different. The second training objective that works at the mini-batch level aims at creating a uniform distribution of the target class predictions by maximizing the entropy of the expectation of the target class predictions. We show through extensive empirical evaluations that the proposed architecture and training objectives significantly improve the performance of the original algorithm. Furthermore, this method also outperforms the state-of-the-art techniques in most unsupervised domain adaptation tasks.

Conference Presentation

Citation Download Citation

Prasanna Reddy Pulakurthi, Sohail A. Dianat, Majid Rabbani, Suya You, and Raghuveer M. Rao "Unsupervised domain adaptation using feature aligned maximum classifier discrepancy", Proc. SPIE 12227, Applications of Machine Learning 2022, 1222707 (3 October 2022); https://doi.org/10.1117/12.2646422

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