Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 7: Good paper, accept
review_content: The paper proposes a method to reconstruct the latent code vector corresponding to a given observation under a GAN generative model. Unlike BiGAN and ALI, this does not require a different training procedure or an additional inference network. Instead, projected gradient descent in code space is used, with an additional heuristic called "stochastic clipping" that randomly resets code components that hit the boundary.

The proposed technique is simple, but the fidelity of the reconstructed images looks good. The "stochastic clipping" technique is interesting, but quite ad-hoc. 

If the goal is to simply keep codes away from the boundary, the authors could also try a simple entropy regularization term (map each code coordinate to [0,1] and use a Bernoulli entropy), as a barrier function for the constraint set. Additionally, one could combine this entropy regularization and the projection step by using exponentiated gradient (Warmuth, 1997), a.k.a. entropic mirror descent (Beck and Teboulle, 2003), which might simplify the algorithm.

For a full conference submission, the authors should explore these things as well as evaluate on more tasks than reconstruction, like classification on the code vectors (as explored in the BiGAN and ALI papers).

Small notes: in Section 2, many uses of the plural term "minima" where the singular term "minimum" should be used.

review_title: Review

final_score: rating: 6: Marginally above acceptance threshold
review_content: The authors present a heuristic for reliably recovering the latent state vector that generates a GAN sample. It seems to work well in practice, providing accurate recovery up to the level of accuracy measured.

Specific comments:
On the topic of reconstructing latent states from GAN samples, the authors should probably include references to BiGAN and adversarially-learned inference, which are slightly different but still closely related. Metz, Poole, Pfau and Sohl-Dickstein (2017) also reconstruct latent states from pixels, but are more focused on accurately generating out-of-sample images than reconstructing the latent state.

The clipping heuristic used is more formally known as a case of projected or proximal gradient descent in the optimization literature. Probably worth putting it in the right context. The heuristic of randomly resetting any index that goes out of bounds is novel as far as I know.

Given regular gradient descent with no clipping is still 98% effective according to their metrics, a more stringent measure of accuracy which more clearly demonstrates the gap between the baseline and the proposed method would be helpful.
review_title: Nice simple heuristic

Decision: Accept