Distinction between features extracted using deep belief networks
Authors: mohammad pezeshki, Sajjad Gholami, Ahmad Nickabadi
ICLR 2014

Abstract: Data representation is an important pre-processing step in many machine learning algorithms. There are a number of methods used for this task such as Deep Belief Networks (DBNs) and Discrete Fourier Transforms (DFTs). Since some of the features extracted using automated feature extraction methods may not always be related to a specific machine learning task, in this paper we propose two methods in order to make a distinction between extracted features based on their relevancy to the task. We applied these two methods to a Deep Belief Network trained for a face recognition task.


Peer review:
[Review]:
Two methods are proposed for making a distinction b/t extracted features based on relevancy. The first method is looking at the variance of hidden nodes when inputs vary on some aspects but stay the same in others.  The second method is looking at the relative activity of the hidden nodes when additional input features are subsequently added. The hope is to identify some “Grandmother” like cells in a DBN which would be selective of faces and digits.

Overall I think the paper is somewhat interesting in it’s motivation of probing the DBN to see the meaning of each hidden layer units. However, DBNs are notoriously hard to interpret due to the fact that it is a distributed representation. While i think the paper is in the right direction, the method presented are very simplistic and it is not clear what can be concluded. In particular, it is somewhat expected that if we remove the nodes which are modified by when a face image changes a face+digit, then what remains can better reconstruct the face. Is training performed sequentially on the three datasets 1,2, and 3?

Another issues is that the digit is just so much brighter than the face, making this problem easier, what happens if you use the digit with the similar intensity as the face? my suspicion is that the DBN would have a lot of trouble with that.

Possible way to make this a good paper would be to use the hidden node discovered as a form of detector. It would perform worse than a discriminatively trained detector but it would be interesting to see a comparison.