Human observers can learn to recognize new categories of objects from a handful of examples, yet doing so with machine perception remains an open challenge. We hypothesize that data-efficient recognition is enabled by representations which make the variability in natural signals more predictable, as suggested by recent perceptual evidence. We therefore revisit and improve Contrastive Predictive Coding, a recently-proposed unsupervised learning framework, and arrive at a representation which enables generalization from small amounts of labeled data. When provided with only 1% of ImageNet labels (i.e. 13 per class), this model retains a strong classification performance, 73% Top-5 accuracy, outperforming supervised networks by 28% (a 65% relative improvement) and state-of-the-art semisupervised methods by 14%. We also find this representation to serve as a useful substrate for object detection on the PASCAL-VOC 2007 dataset, approaching the performance of representations trained with a fully annotated ImageNet dataset.
The authors augment contrastive predictive coding (CPC), a recent representation learning technique organized around making local representations maximally useful for predicting other nearby representations, and evaluates their augmented architecture in several image classification problems. Although the modifications to CPC aren't particularly original, the authors show first that these yield a significant improvement in linear classification accuracy. They then use this improved model to obtain impressive performance in classification within semi-supervised and transfer learning settings, giving strong support for the use of such methods within image processing applications.
Pros:
Owing to its generality (CPC assumes only a weak spatial prior in the input data), and cheap computational cost relative to earlier generative approaches, CPC is already a promising unsupervised representation learning technique. The paper gives more evidence of this usefulness for image data, yielding leading performance on several different image classification benchmarks.
The authors also make the observation that linear separability, the standard benchmark for evaluating unsupervised representations, correlates poorly with efficient prediction in the presence of limited labeled data. This observation should be of interest in the broader community, and points to the need for more diverse metrics for unsupervised representations.
Cons:
The improvements given in the paper are quite useful within their stated domain (image data), but aren't directly applicable to other types of input data. Although the authors make a point of emphasizing the relevance of CPC for other problem domains, they don't currently provide any suggestions for how this current work could be generalized to handle these other cases. In this sense, I think it is a bit deceptive to refer to their model as "CPC v2", as the majority of their changes have no bearing on the intrinsic CPC algorithm itself.
I am sure that some of the methods used here could lead to improvements in the use of CPC for other types of data, but the authors currently don't provide any insight on this issue. In line with that, I think their work would be improved by some commentary on this, in particular by any concrete suggestions they have about how similar augmentations to CPC could be carried out in text, audio, and/or video data.
Verdict:
Owing to the reasons given above, I recommend acceptance.
Minor suggestions:
Please use a different color scheme for your figures that is still meaningful if the paper is printed in greyscale.