Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: This paper proposes to adopt a triplet network as the discriminator in the GANs.
Semi-supervised experiments on MNIST and CIFAR show that this approach can outperform either Triplet Network on labeled data or GANs on unlabeled data.

I am probably not the best reviewer for this paper, but I think this proposed approach could be interesting to ICLR audience and incline to accept this paper.

Question: Are your experimental results directly comparable to the semi-supervised experiments in (Salimans et al, 2016)? If the main point of this paper is to “incorporate discriminator in a metric learning task instead of involving it in classification”, we should have that direct comparison.

Question: is triplet network a well-defined term? “Triplet networks are one of the most commonly used techniques in deep learning metric (Yao et al., 2016; Zhuang et al., 2016). “ It is not used in these two reference papers.

I also would like to see how the accuracies are sensitive to #labeled examples and #features. It’d be desired to add more experimental results. 
review_title: review

final_score: rating: 7: Good paper, accept
review_content: This paper describes using triplet loss to train GAN, and obtained better feature, compared to original GAN and original triplet loss. 

This work can be viewed as extension of  Semi-supervised training with GAN by using triplet loss.

There is a clean gain through the CIFAR-10 experiment. So I recommend to accept. 

review_title: null

Decision: Accept