Latent Space Regularization for Unsupervised Domain Adaptation in Semantic Segmentation

• URL: http://arxiv.org/abs/2104.02633v1
• Date: Tue, 6 Apr 2021 16:07:22 GMT
• Title: Latent Space Regularization for Unsupervised Domain Adaptation in Semantic Segmentation
• Authors: Francesco Barbato, Marco Toldo, Umberto Michieli, Pietro Zanuttigh
• Abstract summary: We introduce feature-level space-shaping regularization strategies to reduce the domain discrepancy in semantic segmentation. We verify the effectiveness of such methods in the autonomous driving setting.
• Score: 14.050836886292869
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep convolutional neural networks for semantic segmentation allow to achieve outstanding accuracy, however they also have a couple of major drawbacks: first, they do not generalize well to distributions slightly different from the one of the training data; second, they require a huge amount of labeled data for their optimization. In this paper, we introduce feature-level space-shaping regularization strategies to reduce the domain discrepancy in semantic segmentation. In particular, for this purpose we jointly enforce a clustering objective, a perpendicularity constraint and a norm alignment goal on the feature vectors corresponding to source and target samples. Additionally, we propose a novel measure able to capture the relative efficacy of an adaptation strategy compared to supervised training. We verify the effectiveness of such methods in the autonomous driving setting achieving state-of-the-art results in multiple synthetic-to-real road scenes benchmarks.

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