Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: The idea of using concatenation of layer outputs as input to deeper layers was introduced in supervised learning context in works like Highway Networks. They show that combination of representations from different layers can lead to better discriminative features resulting in better classification performance.

In this work a similar approach is used in the context of unsupervised clustering. A deep convolutional autoencoder is trained on the data and combination of multiple layer representations is used for clustering. The approach seems quite intuitive and straight forward. Regarding the experiments, I have following questions,

assuming that the clustering accuracies is classification performance,

Is the classifier in every experiment a k-NN?
How is k chosen for k-NN and k-means experiments?
What is the performance when k-NN is applied on individual layer representations from convolutional autoencoder rather than on combination?

Also there are previous works which in detail talk of autoencoder methods as clustering approaches. for example,
http://www.jmlr.org/proceedings/papers/v27/baldi12a/baldi12a.pdf



review_title: more details on the experimentation required

final_score: rating: 4: Ok but not good enough - rejection
review_content: Even though I enjoy the main idea of concatenating multiple layer information into an embedding for the purpose of clustering, I think this papers needs more work before acceptance.

- As I pointed out earlier, the presentation of the paper is a bit confusing.
- Some of the experiments and some of the follow-up discussion does not seem faithful to the task of clustering. Directly passing concatenated embeddings to kNN might be used to measure the quality of those with respect to classification, but this skips clustering step entirely. Instead of this, one can use clustering performance directly (using appropriate metrics) or by clustering first into K clusters and then using cluster assignments as the instance representation used during classification.
- Following the point above, when you say you don't need to know the number of clusters beforehand because you pass embeddings directly to the classification method, I don't follow. What constitutes a cluster in this instance?
- I was also curious about how it would perform when you have only the topmost encoding layer (or an arbitrary one) compared to using all, since you claim in Section 2.2. this way yields more complex representations?
review_title: null

Decision: Reject