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ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation

Abstract : Semantic segmentation is a key problem for many computer vision tasks. While approaches based on convolutional neural networks constantly break new records on different benchmarks, generalizing well to diverse testing environments remains a major challenge. In numerous real world applications, there is indeed a large gap between data distributions in train and test domains, which results in severe performance loss at run-time. In this work, we address the task of unsupervised domain adaptation in semantic segmentation with losses based on the entropy of the pixel-wise predictions. To this end, we propose two novel, complementary methods using (i) entropy loss and (ii) adversarial loss respectively. We demonstrate state-of-the-art performance in semantic segmentation on two challenging "synthetic-2-real" set-ups and show that the approach can also be used for detection.
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Preprints, Working Papers, ...
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https://hal.archives-ouvertes.fr/hal-01942465
Contributor : Tuan-Hung Vu Connect in order to contact the contributor
Submitted on : Monday, December 3, 2018 - 11:26:49 AM
Last modification on : Saturday, December 4, 2021 - 4:01:47 AM

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  • HAL Id : hal-01942465, version 1
  • ARXIV : 1811.12833

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Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Matthieu Cord, Patrick Pérez. ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation. 2018. ⟨hal-01942465⟩

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