Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 7: Good paper, accept
review_content: The paper provides generalization bounds for a common practice in transfer learning with deep neural nets, where the representation learned on a source task (having lot of labeled data) is transferred to a target task. It analyzes two settings: (i) when representation learned on the source is kept fixed and a new classifier for the target task is learned on top of it, (ii) when the representation is also fine-tuned for the target task. To the best of my knowledge it seems to be the first work to analyze this setting. 

Pros:
- considers an important problem
- well-written paper

Cons:
- hard to say if the proof techniques used are novel -- not enough details
- doesn't give much intuition on when is fine tuning better than fixed representation
review_title: reasonable first step for an important problem

final_score: rating: 6: Marginally above acceptance threshold
review_content: This paper proposes sufficient conditions for success of transfer learning
pros:
originality: To the best of my knowledge, this work is original
significance: transfer learning has lead to considerable improvement in deep learning and a theoretical approach for formulating when and how it succeeds is very important and much needed

cons.
clarity: the paper is not completely well-written and in places hard to follow
quality: overall, I like this paper due to the problem it considers and its approach, however, the paper would improve significantly with filling in the gaps mentioned below:
* The authors provide no intuition or insight into how the bound is derived and what the different terms mean, e.g., how does function w look in practice for different datasets? what are the ways to measure or approximate it?
* The assumptions are given without any justification of why they are needed and what are the cases that the hold. e.g., The property that is assumed in Theorem 1. Section 4: assuming that lower level features are more transferrable.
* There are some vague terms used right before Theorem 1 that are not appropriate for a theory paper:  If w does not grow too "quickly", \hat{R} is "small", etc

review_title: A new theoretical angle into transfer learning

Decision: Accept