Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 4: Ok but not good enough - rejection
review_content: CONTRIBUTIONS

This paper applies adversarial auto-encoders to unsupervised classification of MNIST digits.

The proposed model, named DAC for "deep adversarial clustering", optimizes a loss with three components:
- A reconstruction term encourages reconstructions to be close to the original.
- A log-likelihood term on the latent representation's prior distribution forces the prior to track the encoder's distribution.
- An adversarial loss forces the encoder to have a distribution close to the prior distribution.

The paper claims state-of-the-art on the unsupervised MNIST clustering task.

NOVELTY, CLARITY, SIGNIFICANCE, QUALITY

The proposed method is well-explained.

It seems to me that the proposed loss is almost identical to an adversarial auto-encoder loss where the prior would be learned, the difference being that here the prior is learned through maximum likelihood as opposed to using the adversarial term to guide the prior. Is there any reason to choose one over the other?

In my opinion, this work lacks the novelty sought by this workshop: unless something escapes me, the main point of the paper is that a very straightforward adaptation of adversarial auto-encoders performs well at clustering MNIST digits.

PROS (+), CONS (-)

+ Proposed method is well-explained
- Paper offers little novelty
review_title: Review

final_score: rating: 4: Ok but not good enough - rejection
review_content: The paper proposes to use adversarial autoencoders with a Gaussian mixture prior on the latent space for unsupervised clustering. The parameters of the Gaussian mixture are also optimized by maximum likelihood. Training the prior by maximum likelihood while training the rest of the model with the AAE objective appears to be the main novelty of the work. 

The use of a tuned prior appears to give significantly better results on unsupervised clustering than the previous work of Makhzani et al on adversarial autoencoders. Training the prior by maximum likelihood (rather than using the adversarial loss) might be helpful in avoiding optimization difficulties, but there are no experiments exploring this.

The paper shows good results on unsupervised clustering of MNIST digits, but the proposed algorithm is a standard adversarial autoencoder with a learned prior term. The main concern is that the algorithm does not seem sufficiently novel to justify inclusion in the workshop.
review_title: Review

Decision: Reject