Venue: ICLR 2017 conference

Reviews:

final_score: rating: 7: Good paper, accept
review_content: This paper provides an interesting idea, which extends GAN by taking into account bidirectional network. Totally, the paper is well-written, and easy to follow what is contribution of this paper. From the theoretical parts, the proposed method, BiGAN, inherits similar properties in GAN. The experimental results show that BiGAN is competitive with other methods. A drawback would a non-convex optimization problem in BiGAN, this paper is still suitable to be accepted in my opinion. 
review_title: An interesting idea

final_score: rating: 7: Good paper, accept
review_content: This is a parallel work with ALI.  The idea is using auto encoder to provide extra information for discriminator. This approach seems is promising from reported result. For feature learning part of BiGAN, there still is a lot of space to improve, compare to standard supervised convnet. 
review_title: null

final_score: rating: 7: Good paper, accept
review_content: The authors extend GANs by an inference path from the data space to the latent space and a discriminator that operates on the joint latend/data space. They show that the theoretical properties of GANs still hold for BiGAN and evaluate the features learned unsupervised in the inference path with respect to performance on supervised tasks after retraining deeper layers.

I see one structural issue with this paper: Given that, as stated in the abstract, the main purpose of the paper is to learn unsupervised features (and not to improve GANs), the paper might spent too much space on detailing the relationship to GANs and all the theoretical properties. It is not clear whether they actually would help with the goal of learning good features. While reading the paper, I actually totally forgot about the unsupervised features until they reappeared on page 6. I think it would be helpful if the text of the paper would be more aligned with this main story.

Still, the BiGAN framework is an elegant and compelling extension to GANs. However, it is not obvious how much the theoretical properties help us as the model is clearly not fully converged. To me, especially Figure 4 seems to suggest that G(E(x)) might be doing not much more than some kind of nearest neighbour retrival (and indeed one criticism for GANs has always been that they might just memorize some samples). By the way, it would be very interesting to know how well the discriminator actually performs after training.

Coming back to the goal of learning powerful features: The method does not reach state-of-the-art performance on most evaluated tasks (Table 2 and 3) but performs competitive and it would be interesting to see how much this improves if the BiGAN training (and the convolutional architecture used) would be improved.

The paper is very well written and provides most necessary details, although some more details on the training (learning rates, initialization) would be helpful for reproducing the results.

Overall I think the paper provides a very interesting framework for further research, even though the results presented here are not too impressive both with respect to the feature evaluation (and the GAN learning).

Minor: It might be helpful to highlight the best performance numbers in Tables 2 and 3.
review_title: Nice work, some structural issues

final_score: rating: null
review_content: For BiGAN,  G(E(x)) seems produces reasonable output similar to x. Do you observe improvement of diversity of G by using Z? Also, Z is usually a uniform distribution, but seems E(x) should be very different to Z. What is relation of E(G(Z)) to Z?

review_title: Prereview Question

final_score: rating: null
review_content: Given that you have the encoder from the pixel space to the latent space, I wonder whether it is possible to calculate log-likelihoods of the data under the model. As shown by Theis et al (https://arxiv.org/abs/1511.01844),  evaluating only on samples as indication of a good model fit is very problematic. I would be very interested to see how the model compares to other models like Pixel-RNN in terms of likelihood.

An additional question regarding Table 2: For the two rows about your model, I understand that you retraing the deeper layers supervised on ImageNet. However I am not sure how to interpret the rows above. Which networks exactly did you freeze and retrain here? If it is other networks than your own, do they have the same architecture (e.g. conv1-conv5)?
review_title: Questions

Meta-review: All reviewers unanimously praised the novelty and quality of the paper. Minor revisions, following the reviewers' suggestions, will make the paper even better. 
Decision: Accept (Poster)