Hi. The paper says there are two encoders (generators) for the clean image and noisy image splitly. However, I can only find one generator 'gen_a' in 'train.py'. I think that it is the generator for noisy image. Where is the defination of the other generator (for clean image)? Thansk in advance.

Does the clean data set come from the VOC data set? So do you need to search for noise data by yourself? Where is the noise data set used by the author? Is the clean data set put into Celeba_A, and then the noise data set into Celeba_B?

As your attempt, you want to divide the pascalvoc dataset into 4, and this .py file is just for pascalvoc dataset. But in your code : def splitlist(folder, trainfolder1, trainfolder2, testfolder): if not os.path.exists(folder): raise Exception('input folder dose not exist ! please check it !!') if not os.path.exists(trainfolder1): os.makedirs(trainfolder1) if not os.path.exists(trainfolder2): os.makedirs(trainfolder2) if not os.path.exists(testfolder): os.makedirs(testfolder)

filelist = os.listdir(folder)
sublist = random.sample(filelist, **110000**)
sublist_a = random.sample(sublist, **55000**)
   .....

The number of pictures in the folder is 110000 and 55000 respectively, but there are only 12031 images in the pascalvoc data set. How did you do it? Or is it that you made a mistake?

in dis_update(self, x_a, x_b, hyperparameters): the content adversial codes are , out_a = self.dis_content(h_a) out_b = self.dis_content(h_b) why not use h_a.detach() and h_b.detach() to avoid the update in the parameters of G ? Thank you!