ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
This addresses the challenge of performance loss in semantic segmentation when training and test data distributions differ, which is critical for real-world applications like autonomous driving, though it is an incremental improvement over existing domain adaptation techniques.
The paper tackles the problem of domain adaptation in semantic segmentation by proposing two methods based on entropy and adversarial losses, achieving state-of-the-art performance on synthetic-to-real setups and demonstrating applicability to detection tasks.
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.