Beyond Deterministic Translation for Unsupervised Domain Adaptation

• URL: http://arxiv.org/abs/2202.07778v1
• Date: Tue, 15 Feb 2022 23:03:33 GMT
• Title: Beyond Deterministic Translation for Unsupervised Domain Adaptation
• Authors: Eleni Chiou and Eleftheria Panagiotaki and Iasonas Kokkinos
• Abstract summary: In this work we challenge the common approach of using a one-to-one mapping (''translation'') between the source and target domains in unsupervised domain adaptation (UDA) Instead, we rely on translation to capture inherent ambiguities between the source and target domains. We report improvements over strong recent baselines, leading to state-of-the-art UDA results on two challenging semantic segmentation benchmarks.
• Score: 19.358300726820943
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work we challenge the common approach of using a one-to-one mapping ('translation') between the source and target domains in unsupervised domain adaptation (UDA). Instead, we rely on stochastic translation to capture inherent translation ambiguities. This allows us to (i) train more accurate target networks by generating multiple outputs conditioned on the same source image, leveraging both accurate translation and data augmentation for appearance variability, (ii) impute robust pseudo-labels for the target data by averaging the predictions of a source network on multiple translated versions of a single target image and (iii) train and ensemble diverse networks in the target domain by modulating the degree of stochasticity in the translations. We report improvements over strong recent baselines, leading to state-of-the-art UDA results on two challenging semantic segmentation benchmarks.

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