Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: The paper introduces a combination of transfer learning based on pre-trained DNNs with associative memory.
The idea itself makes sense and I am surprised no one has explored it before.

However, the results seem to be relatively poor and thus the method does not seem to be very valuable as is.
review_title: Important topic, but surprisingly modest performance

final_score: rating: 5: Marginally below acceptance threshold
review_content: The paper proposes a technique for doing one-shot learning in computation/memory constrained settings, by taking features from a pre-trained CNN, product-quantizing them (with random anchors) to obtain an alphabet, and linking "words" from this alphabet (the PQ chunk centers comprising used to approximate a given feature vector) with class labels via a binary associative memory.

The setting is interesting and basic idea is (to the best of my knowledge) novel and appealing. However the experimental results aren't compelling and lack natural baselines. At least as a reference, it would have been nice to see the performance of a simple linear classifier trained on the feature vectors, if not a comparison against previous techniques that attempt to learn such linear classifiers incrementally. As it is it is hard to judge the merit of the proposed approach relative to the state of the art, and on their own the results are somewhat underwhelming.
review_title: Elegant approach, but poor performance and missing baselines

Decision: Reject