An Adversarial Self-Learning Method for Cross-City Adaptation in Semantic Segmentation

Semantic segmentation is an important task in the visual system of self-driving cars. The semantic segmentation models based on the CNN (Convolutional Neural Network) trained with the large numbers of annotated labels may not work well at the environments different from the training sets due to the domain gap between the train and test domains. Just for the reduction of the distance between the source and target domains, domain adaptation methods are proposed for the unsupervised training with the unlabeled target domain. Not only the reduction of the domain-shift, but we also propose the self-learning method to enhance the predicted probabilities of the target domain. To gain more accurate probability maps of the target domain generated from the segmentation model which is trained by the source domain, we propose the adversarial self-learning method which is consists of the domain adaptation part and self-learning part. The adversarial self-learning method can maximize the predicted probabilities for the probability maps of the target domain gained from the segmentation model which is adapted with the domain adaptation method before the self-learning. With the Cityscapes to NTHU cross-city adaptation experiments, we can see that the adversarial self-learning method can achieve stateof-the-art results compared with the domain adaptation methods proposed in the recent researches.