Joint Adversarial Learning for Domain Adaptation in Semantic Segmentation

Abstract

Unsupervised domain adaptation in semantic segmentation is to exploit the pixel-level annotated samples in the source domain to aid the segmentation of unlabeled samples in the target domain. For such a task, the key point is to learn domain-invariant representations and adversarial learning is usually used, in which the discriminator is to distinguish which domain the input comes from, and the segmentation model targets to deceive the domain discriminator. In this work, we first propose a novel joint adversarial learning (JAL) to boost the domain discriminator in output space by introducing the information of domain discriminator from low-level features. Consequently, the training of the high-level decoder would be enhanced. Then we propose a weight transfer module (WTM) to alleviate the inherent bias of the trained decoder towards source domain. Specifically, WTM changes the original decoder into a new decoder, which is learned only under the supervision of adversarial loss and thus mainly focuses on reducing domain divergence. The extensive experiments on two widely used benchmarks show that our method can bring considerable performance improvement over different baseline methods, which well demonstrates the effectiveness of our method in the output space adaptation.