Venue: ICLR 2017 conference

Reviews:

final_score: rating: null
review_content: The experimental results depicted in Figure 8 are very intriguing. Have you tried using addition instead of concatenation and running the same experiment? It would be very interesting to compare the two scenarios, especially since the concatenation operation seems to have made the first layers of the network seem "not very useful, increasingly so as approaching the concatenation point".

Regarding the Inception experiments, have you considered adding an additional trainable transform layer before the softmax? This could reduce the feature dimensionality and make the problem more feasible (of course at the cost of not being able to use just a linear classifier on the original features). One additional question regarding the Inception case is whether Pooling operations should be used before feeding the features to the linear classifier, as an alternative way to look at the problem.
review_title: Pre-review questions

final_score: rating: null
review_content: This paper is essentially to test the classification rate based on the features from different layers of the neural network, which has been explored in many early works, e.g., DECAFF.  What  technical difference does this paper make?
The experimental results in this paper show that 1) (Fig 5a) too many random layers are harmful. 2) (Fig 5b) training is helpful. 3) (Fig 7) lower layers converge faster than higher layer. 4) (Fig 8) too deep network is hard to train, and skip link can remedy this problem. They are all well-known conclusions, and more comprehensive discussions have been made in previous work. Can the linear classifier probe provide some new explanations for these phenomena? Based on the paper's observations, can we get some new clues to design better neural network architectures?
The analysis in this paper is all for classification network. Can the linear classifier probe be useful for non-classification model, e.g., image generation networks, regression-based localization networks?
I got a feeling that this paper somewhat wants to overwrite the definition of information, as a particular technical term, using the linear classifier. However, the linear classifier is a particular discriminative formulation driven by particular loss function and particular data. Is it general enough as a measurement for information? And is it possible to provide some theoretical justification.
In general, the intro arguments of this paper is reasonable to me, but I have the above questions about the technical soundness and significance. 
review_title: Novelty and usefulness of the linear probes

final_score: rating: 4: Ok but not good enough - rejection
review_content: This paper proposes to use a linear classifier as the probe for the informativeness of the hidden activations from different neural network layers. The training of the linear classifier does not affect the training of the neural network. 

The paper is well motivated for investigating how much useful information (or how good the representations are) for each layer. The observations in this paper agrees with existing insights, such as, 1) (Fig 5a) too many random layers are harmful. 2) (Fig 5b) training is helpful. 3) (Fig 7) lower layers converge faster than higher layer. 4) (Fig 8) too deep network is hard to train, and skip link can remedy this problem.

However, this paper has following problems:

1. It is not sufficiently justified why the linear classifier is a good probe. It is not crystal clear why good intermediate features need to show high linear classification accuracy. More theoretical analysis and/or intuition will be helpful.   
2. This paper does not provide much insight on how to design better networks based on the observations. Designing a better network is also the best way to justify the usefulness of the analysis.

Overall, this paper is tackling an interesting problem, but the technique (the linear classifier as the probe) is not novel and more importantly need to be better justified. Moreover, it is important to show how to design better neural networks using the observations in this paper.
  

review_title: Interesting problem, but technically and experimentally not solid enough

final_score: rating: 5: Marginally below acceptance threshold
review_content: This paper proposes a method that attempts to "understand" what is happening within a neural network by using linear classifier probes which are inserted at various levels of the network.

I think the idea is nice overall because it allows network designers to better understand the representational power of each layer in the network, but at the same time, this works feels a bit rushed. In particular, the fact that the authors did not provide any results in "real" networks, which are used to win competitions makes the results less strong, since researchers who want to created competitive network architectures don't have enough evidence from this work to decides whether they should use it or not.

Ideally, I would encourage the authors to consider continuing this line of research and show how to use the information given by these linear classifiers to construct better network architectures. 

Unfortunately, as is, I don't think we have enough novelty to justify accepting this work in the conference. 
review_title: Final review

final_score: rating: 4: Ok but not good enough - rejection
review_content: The authors propose a method to investigate the predictiveness of intermediate layer activations. To do so, they propose training linear classifiers and evaluate the error on the test set.

The paper is well motivated and aims to shed some light onto the progress of model training and hopes to provide insights into deep learning architecture design.

The two main reasons for why the authors decided to use linear probes seem to be:
- convexity
- The last layer in the network is (usually) linear

In the second to last paragraph of page 4 the authors point out that it could happen that the intermediate features are useless for a linear classifier. This is correct and what I consider the main flaw of the paper. I am missing any motivation as to the usefulness of the suggested analysis to architecture design. In fact, the example with the skip connection (Figure 8) seems to suggest that skip connections shouldn't be used. Doesn't that contradict the recent successes of ResNet?

While the results are interesting, they aren't particularly surprising and I am failing to see direct applicability to understanding deep models as the authors suggest.
review_title: Linear predictiveness of intermediate layer activations.

Meta-review: The reviewers generally agreed that the research direction pursued in the paper is a valuable one, but all reviewers expressed strong reservations about the value of a linear probe on intermediate features. The lack of experiments on more complex state-of-the-art networks is also potentially problematic. In the end, it seems that there is insufficient evidence that the proposed approach is actually a useful tool in its present state, though the authors should be encouraged to pursue this line of research further.
Decision: Reject