OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
Authors: Michael Mathieu, Yann LeCun, Rob Fergus, David Eigen, Pierre Sermanet, Xiang Zhang
ICLR 2014

Abstract: We present an integrated framework for using Convolutional Networks for classification, localization and detection. We show how a multiscale and sliding window approach can be efficiently implemented within a ConvNet. We also introduce a novel deep learning approach to localization by learning to predict object boundaries. Bounding boxes are then accumulated rather than suppressed in order to increase detection confidence. We show that different tasks can be learnt simultaneously using a single shared network. This integrated framework is the winner of the localization task of the ImageNet Large Scale Visual Recognition Challenge 2013 (ILSVRC2013), and produced near state of the art results for the detection and classifications tasks. Finally, we release a feature extractor from our best model called OverFeat.


Peer review:
[Review]:
We would like to thank all the reviewers for their comments and feedback.  We have integrated many of the suggestions into a new version (v4) of the paper, and are continuing to make revisions.  This version has been submitted to ArXiv and will appear later today, on Tue, 25 Feb 2014 01:00:00 GMT.


In response to your comments:


- 'some of the tables and minor notes ... could be moved to an appendix'; 
- 'essential ideas should be distilled ... and the details ... relegated to an appendix'

Thanks for these suggestions.  We are currently working to factor out the details and make the paper more succinct.  Some progress on this has already been made in the newest version (v4), and we are now working on another revision with further editing.



- 'exposition on “dense application” and why this is a computational savings is less clear than it could be'
- 'clarity of exposition in certain parts'

This section has been updated in the new version (v4), and should be clearer.  Many other parts of the text have also been revised, and we are continuing to make edits for this.



- 'Some discussion of not just which hyperparameters were chosen but how and why'
- 'rough idea of the relative importance of horizontal flipping and other tricks described in 3.3, it would be useful to know'
- 'more detail on computational efficiency/accuracy compromise'

Thanks for the suggestions.  We will try to discuss some of these questions more in the next revision (v5) of the paper (this has not yet been included in v4).  We do not have systematic comparisons for many of these, though, but further studies of them could make good followup work.



- 'results on PASCAL'

Thanks for the suggestion.  This is likely not something we will be able to get done for this paper, but we agree it would be interesting to see, and may look at this in the future.