Domain adaptation

Deep learning and reinforcement learning are key technologies for autonomous driving. One of the challenges they face is to adapt to conditions which differ from those met during training. To improve systems’ performance in such situations, we explore so-called “domain adaptation” techniques, as in AdvEnt at CVPR’19 and DADA its extension at ICCV’19.

Publications

xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation

Maximilian Jaritz, Tuan-Hung Vu, Raoul de Charette, Émilie Wirbel, and Patrick Pérez
Computer Vision and Pattern Recognition (CVPR), 2020


DADA: Depth-aware Domain Adaptation in Semantic Segmentation

Tuan Hung Vu, Himalaya Jain, Maxime Bucher, Matthieu Cord, and Patrick Pérez
International Conference on Computer Vision (ICCV), 2019


AdvEnt: Adversarial Entropy minimization for domain adaptation in semantic segmentation

Tuan Hung Vu, Himalaya Jain, Maxime Bucher, Matthieu Cord, and Patrick Pérez
Computer Vision and Pattern Recognition (CVPR), 2019