Venue: ICLR 2018 conference

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: This paper presents a method based on GANs for visualizing how humans represent visual categories. Authors perform experiments on two datasets: Asian Faces Dataset and ImageNet Large Scale Recognition Challenge dataset.

Positive aspects:
+ The idea of using GANs for this goal is smart and interesting
+ The results seem interesting too

Weaknesses:
- Some aspects of the paper are not clear and presentation needs improvement.
- I miss a clearer results comparison with previous methods, like Vondrick et al. 2015.

Specific comments and questions:

-  Figure 1 is not clear. Authors should clarify how they use the inference network and what the two arrows from this inference network represent.
- Figure 2 is also not clear. Just the FLD projections of the MCMCP chains are difficult to interpret. The legend of the figure is too tiny. The right part of the figure should be better described in the text or in the caption, I don't understand well what this illustrates.
- Regarding to the human experiments with AMT: how do the authors deal with noise on the workers performance? Is any qualification task used? What are the instructions given to the workers?
- In section 4.2. the authors state "We also simultaneously learn a corresponding inference network, .... granular human biases captured". This seems interesting but I didn't find any result on that in the paper. Can you give more details or refer to where in the paper it is discussed/tested?
- Figure 4 shows "most interpretable mixture components".  How this "most interpretable" were selected?
- In second paragraph Section 4.3, it should be Table 1 instead of Figure 1. 
- It would be interesting to see a discussion on why MCMCP Density is better for group 1 and MCMCP Mean is better for group 2. To see the confusion matrixes could be useful.

I like this paper. The addressed problem is challenging and the proposed idea seems interesting.  However, the aspects mentioned make me think the paper needs some improvements to be published.

review_title: Using GANs for visualizing human representations of visual categories.

final_score: rating: 6: Marginally above acceptance threshold
review_content: Quality

This paper demonstrates that human category representations can be inferred by sampling deep feature spaces. The idea is an extension of the earlier developed MCMC with people approach where samples are drawn in the latent space of a DCGAN and a BiGAN. The approach is thoroughly validated using two online behavioural experiments.

Clarity

The rationale is clear and the results are straightforward to interpret. In Section 4.2.1 statements on resemblance and closeness to mean faces could be tested. Last sentences on page 7 are hard to parse. The final sentence probably relates back to the CI approach. A few typos.

Originality

The approach is a straightforward extension of the MCMCP approach using generative models.

Significance 

The approach improves on previous category estimation approaches by embracing the expressiveness of recent generative models. Extensive experiments demonstrate the usefulness of the approach.

Pros

Useful extension of an important technique backed up by behavioural experiments.

Cons

Does not provide new theory but combines existing ideas in a new manner.
review_title: deep extension of MCMCP

final_score: rating: 5: Marginally below acceptance threshold
review_content: The idea of using MCMCP with GANs is well-motivated and well-presented
in the paper, and the approach is new as far as I know.  Figures 3 and 5 are
convincing evidence that MCMCP compares favorably to direct sampling of
the GAN feature space using the classification images approach.

However, as discussed in the introduction, the reason an efficient
sampling method might be interesting would be to provide insight
on the components of perception.  On these insights, the paper felt
incomplete.

For example, it was not investigated whether the method identifies
classification features that generalize.  The faces experiment is
similar to previous work done by Martin (2011) and Kontsevich
(2004) but unlike that previous work does not investgiate whether
classification features have been identified that can be added to an
arbitrary image to change the attribute "happy vs sad" or "male vs female".

Similarly, the second experiment in Table 1 compares classification
accuracy between different sampling methods, but it does not provide
any comparison as done in Vondrick (2015) to a classifier trained
in a conventional way (such as an SVM), so it is difficult to discern
whether the learned distributions are informative.

Finally, the effect of choosing GAN features vs a more "naive" feature
space is not explored in detail.  For example, the GAN is trained
on an image data set with many birds and cars but not many
fire hydrants.  Is the method giving us a picture of this data set?
review_title: This paper proposes a method for using MCMCP to characterize a distribution of GAN-constrained image statistics corresponding to a human concept category. The paper describes a well-motivated method for investigating an interesting problem, but feels incomplete in exploring natural questions presented by the problem.

Meta-review: This paper introduces a GAN-based framework for inferring human category representations. The reviewers agree that the idea is interesting and well-motivated, and the results are promising. The technical contribution is not significant, but nevertheless the paper combines existing ideas in an interesting way. The reviewers would also like to see some more work towards the direction of investigation of the results and extraction of insights, without which the paper feels somehow incomplete.
Decision: Invite to Workshop Track