Self-training for unsupervised domain adaptation

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2022-8-31

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Akkaya, İbrahim Batuhan

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Despite the outstanding performance of deep learning techniques, achieving high per- formance generally demands large amounts of labeled data. Because of the labeling costs, people consider utilizing public datasets or synthetic images with freely gen- erated labels. Unfortunately, deep neural networks are notably sensitive to domain misalignment. The methods to reduce domain misalignment are studied under do- main adaptation (DA). Self-training, which selects a subset of the unlabeled data for pseudo-labeling, has been exploited for DA methods lately. These studies usually ex- ploit a confidence threshold to eliminate inaccurate pseudo-labels. Confidence-based approaches rely on the low-density separation hypothesis, which assumes data is in- dependent and identically distributed. However, the low-density separation hypoth- esis for the target domain for the model trained in the source domain may not hold since the source, and target domains do not share the same distribution. This situation reveals the necessity of a pseudo-labeling metric specific to the target domain. In this thesis, we propose several self-training-based unsupervised DA methods. We evaluate our methods in different modalities such as visible and thermal spectrum, for different tasks such as classification and semantic segmentation, and in different sce- narios such as classical and source-free DA. First, we propose a self-training guided adversarial DA method to promote the generalization capabilities of adversarial DA methods in thermal to RGB modalities. Then, as the main contribution of this thesis, we design a metric learning approach defined in the target domain to enable better guidance for self-training. We use our metric for semantic segmentation tasks in clas- sical and source-free DA scenarios. The experimental results show the superiority of the proposed metric and the effectiveness of the self-training.

Subject Keywords

Domain adaptation, Metric learning, Self-training, Adversarial training

URI

https://hdl.handle.net/11511/99420

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

İ. B. Akkaya, “Self-training for unsupervised domain adaptation,” Ph.D. - Doctoral Program, Middle East Technical University, 2022.