Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: This work proposes a new method of stabilizing GAN training and encouraging the generator to cover the data distribution better (i.e. less mode dropping).  As I understand it they do so by gradually annealing the data distribution so that it initially has high entropy and gradually move towards the true distribution. 

While this approach is potentially promising, the paper in its current form lacks and discussion of its relation to other approaches to stabilizing GANs. I think this method needs to be placed in context to be better understood.

Finally, I have one question: in algorithm 1, the generator network is written as a function of beta, but as I understand it only the data distribution is annealed, not the generator distribution? Please explain.
review_title: relation and comparisons to other work missing 

final_score: rating: 4: Ok but not good enough - rejection
review_content: The authors present a heuristic for improving the stability of training GANs by annealing the data distribution. With the exception that they do not add noise to samples from the generator (though I could be mistaken) the method seems identical to the instance noise heuristic proposed by Sonderby et al (http://www.inference.vc/instance-noise-a-trick-for-stabilising-gan-training/) and analyzed formally in Arjovsky and Bottou (2017), section 3. The authors do not discuss this connection anywhere. The paper also seems to be missing a discussion section. If these omissions were fixed and the contribution beyond instance noise were explained then this paper could be considered for acceptance, but not in its current state.
review_title: Fails to address the relevant prior literature

Decision: Reject