Self-training via Metric Learning for Source-Free Domain Adaptation of Semantic Segmentation

• URL: http://arxiv.org/abs/2212.04227v2
• Date: Tue, 9 Apr 2024 13:30:15 GMT
• Title: Self-training via Metric Learning for Source-Free Domain Adaptation of Semantic Segmentation
• Authors: Ibrahim Batuhan Akkaya, Ugur Halici,
• Abstract summary: Unsupervised source-free domain adaptation methods aim to train a model for the target domain utilizing a pretrained source-domain model and unlabeled target-domain data. Traditional methods usually use self-training with pseudo-labeling, which is often subjected to thresholding based on prediction confidence. We propose a novel approach by incorporating a mean-teacher model, wherein the student network is trained using all predictions from the teacher network.
• Score: 3.1460691683829825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised source-free domain adaptation methods aim to train a model for the target domain utilizing a pretrained source-domain model and unlabeled target-domain data, particularly when accessibility to source data is restricted due to intellectual property or privacy concerns. Traditional methods usually use self-training with pseudo-labeling, which is often subjected to thresholding based on prediction confidence. However, such thresholding limits the effectiveness of self-training due to insufficient supervision. This issue becomes more severe in a source-free setting, where supervision comes solely from the predictions of the pre-trained source model. In this study, we propose a novel approach by incorporating a mean-teacher model, wherein the student network is trained using all predictions from the teacher network. Instead of employing thresholding on predictions, we introduce a method to weight the gradients calculated from pseudo-labels based on the reliability of the teacher's predictions. To assess reliability, we introduce a novel approach using proxy-based metric learning. Our method is evaluated in synthetic-to-real and cross-city scenarios, demonstrating superior performance compared to existing state-of-the-art methods.

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