In a conditional GAN, we give a random noise along with a label to the generator as input. In this paper, I don't understand why in one section of the paper, they say they are giving the random noise as input and the in another section of the paper they are saying it is concatenated to the output.
page 2 footnote
page 3 model setup section
little overview of the paper: Code switching is a phenomenon in spoken language where we switch between two different languages. Mixed language models improve the accuracy of automatic speech recognition to higher degree but the problem is less availability of mixed language written sentences. Thus, as a data augmentation technique, a conditional GAN is developed to synthesize English, Mandarin mixed sentences from a pure Mandarin sentence. The trained generator acts as an agent telling which words in the Mandarin sentence have to be translated. It outputs a binary array (of length equal to input Mandarin sentence length). Both generator and discriminator are BLSTM networks.
#####EDIT: The author accepted that it is a typo, noise should be concatenated after the embedding layer not to the output of BLSTM Author's reply: It is a typo in page 3. The noise is concatenated with the output of the embedding layer. Thanks for your correction. #####