[ Supplementary Material ] Learning to Adapt via Latent Domains for Adaptive Semantic Segmentation Anonymous Author(s) Affiliation Address email

AAppendix1 In the supplementary material, we provide more experimental results summarized as follows:2 In A.1, we use ResNet101 as the backbone network and compare our method with state-of-3 the-art methods, demonstrating that our method achieves consistent top results on different4 In A.2, we provide more t-SNE visualization results for a comprehensive analysis on the6 feature space learned from different models.7 In A.3, we study the effect of the image-to-image translation model on the performance of8 domain adaptive semantic segmentation.9 In A.4, we discuss the limitations of our method and provide the URL link of code to10 reproduce the main experimental results.11 "V" and "R" indicate the method using VGG16 and ResNet101 backbone networks, respectively. In the main paper, we report results using VGG1613 as the backbone for both settings: single-target14 and multi-target domain adaptation.