1. While the idea of jointly discovering, hallucinating, and adapting is interesting, there is a complete lack of discussing the impact of adding additional parameters and additional computational effort due to the multi-stage training and the multiple discriminators. The authors should provide this analysis for a fair comparison with the baseline [31, 33, *]. 2. Splitting the target data into easy and hard is already explored in the context of UDA. 3. Discovering the latent domain from the target domain is already proposed in [24]. 4. The problem of Open Compound Domain Adaptation is already presented in [**]. 5. Hallucinating the latent target domains is achieved through an image translation network adapted from [5]. 6. Style consistency loss to achieve diverse target styles has been used in previous works. 7. While the existing UDA methods [31,33] only use one discriminator, it is unclear to me why authors have applied multiple discriminators. 8. The details of the discriminator have not been discussed. 9. I was wondering why including the hallucination part reduces the performance in Table 1(b). It seems like the Discover module with [31] performs better than (Discover + Hallucinate + [31]). Also, the complex adapting stage where the authors used multiple discriminators mostly brings performance improvement. More importantly, did authors try to run the baseline models [17, 25, 31, 33, 39] with a similar longer training scheme? Otherwise, it is unfair to compare with the baselines. 10. Since the authors mentioned that splitting the training process helps to achieve better performance, It could be interesting to see the results of single-stage and multi-stage training. 11. It is not well explained why the adaptation performance drops when K > 3. Also, the procedure of finding the best K seems ad hoc and time-consuming. 12. I am just curious to see how the proposed method performs in a real domain adaptation scenario (GTA5->CityScapes). [*] Fei Pan, Inkyu Shin, François Rameau, Seokju Lee, In So Kweon. Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision. In CVPR 2020. [**] Liu, Ziwei and Miao, Zhongqi and Pan, Xingang and Zhan, Xiaohang and Lin, Dahua and Yu, Stella X. and Gong, Boqing. Open Compound Domain Adaptation. In CVPR 2020.
1. While the idea of jointly discovering, hallucinating, and adapting is interesting, there is a complete lack of discussing the impact of adding additional parameters and additional computational effort due to the multi-stage training and the multiple discriminators. The authors should provide this analysis for a fair comparison with the baseline [31, 33, *].