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]:
This paper demonstrates a convolutional architecture for simultaneously detecting and localizing ImageNet objects. It is the winner of localization task of ILSVRCS2013 competition and that by itself makes it interesting.

They implement a combined architecture where first 5 layers share features compute features that are used for both classification and localization tasks.

A lot of detail goes into constructing an architecture in a way as to make network windows aligned with the objeect. They use 6 scales increased in steps between 1.08 and 1.27 (why this arbitrary choice of steps?) along with 1 pixel offsets and a skip feature connections at the top to prevent the stride from being too large.

This is a solid paper which summarizes significant body of work in neural network design, which makes its relevance to ICLR high.

Some suggestions:
-- I wish the authors gave more detail on computational efficiency/accuracy compromise since that needs to be considered when running in an industrial setting.  For instance, coarse vs fine stride seems to provide 1% absolute improvement, while requiring 9x more computation at the lowest level. How much does that affect total computation? This could be done by adding an extra column 'FLOPS' to Table 5.