weakness] Modeling:
The rewards are designed based on a discriminator. As we know, generative adversarial networks are not easy to train since generative networks and discriminative networks are trained alternatively. In the proposed method, the policy network and the discriminator are trained alternatively. I doubt if it is easy to train the model. I would like to see the training curves for rewards value.
The detailed alignment function used in Eq. (1) and Eq. (3) need to be provided.
Experiment: - The results are not satisfying. In the experiment, the generation quality of the proposed method is not good as traditional generative networks in terms of FID. In the image parsing part, the results are far behind the compared methods. - Since the results are not comparable to the existing methods, there seems not too much significance for the proposed methods.
- Since the results are not comparable to the existing methods, there seems not too much significance for the proposed methods.