Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 7: Good paper, accept
review_content: This paper proposes using linear classifiers (probes) as a measure of semantic information in learned representations. If a linear classifier operating on a combined representation A+B operates just as well as on A alone then representation B is in some sense useless; B carries none of this heuristic type of semantic information.  This is applied to Inception v3, where linear probes increase in accuracy with depth, except when applied to Inception v3's auxiliary head. The method is also applied to a pathologically deep MNIST network with a skip connection in order to show that the first part of the network does not support accurate classifier probes.

It's not clear how to connect the sense in which representation B is useless to specific changes that might improve the capability of non-pathological networks or our understanding of when a network does or does not work. However, this work adds to the set of intuitions we have network internals, so it is likely to contribute to such advancements and should be discussed at the ICLR workshop.


I love the lion example.
review_title: Interesting work, but the potential impact is not clear

final_score: rating: 6: Marginally above acceptance threshold
review_content: This work presents the idea of linear probes, i.e., linear classifiers that measure the class predictability of hidden layers of images. One of their results is that demonstrating the usefulness of the probes in monitoring the behaviour of skip-connections.

As a note, you never really explain in the paper what is the linear classifier classifying, so I had to check the long submission. 

Overall, I'm in favor of the idea of probes. Personally, I'm not so socked that probes are linear, as long as one is careful regarding the conclusions drawn. On this point, I have some reservations in terms of what do the probes really tell us other than "how well I can linearly predict the target class from the hidden layer", which is I think rather limiting in terms of how information like this can be used to improve networks. For this reason, it might be interesting to maybe come up with more than one classifiers (e.g., size of object, color of object, location of object, occluded object, number of objects in the image etc etc) and use these multiple probes as a way to quantify what each layer is better at capturing.  This could complement work in understanding neural networks in a quantitative way, which to the best of my knowledge relies mostly on visualization  (e.g., Zeiler and Fergus, 2014).
 
review_title: null

Decision: Accept