ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation

Understand

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.