Quantum mechanics

World Wide Web

Neuroscience

Entropy (arrow of time)

Cross entropy

Classifier (UML)

Computer science

Image segmentation

Natural language processing

Physics

Segmentation

Psychology

Pattern recognition (psychology)

Minification

Artificial intelligence

Programming language

Deep learning

Domain adaptation

Convolutional neural network

Machine learning

Adaptation (eye)

Adversarial system

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.

ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation