Review for NeurIPS paper: Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation

Weaknesses: - The technical novelty of the proposed method is somewhat incremental since it is largely based on the work from [14] with some modifications to the generator and the discriminator architectures. The word-level training feedback in the discriminator seems to be the main technical contribution, but is not ground-breaking as it extends the auxiliary classifier in conditional GAN with multiple classes (i.e. Specifically, only the nouns and adjectives are chosen manually as text-relevant attributes, which convey a very limited context of general descriptions. Although it may allow a fine-control of the image content in a limited context, it reduces the capability of aligning rich context of the text to the image, often available in approaches learning to encode the whole sentence (e.g. Although authors made some justifications in Section 3.2.1 of using heuristic approach, it does not feel that this assumption holds in general. Current comparisons are mostly focused on ManiGAN.