Deep and Wide Multiscale Recursive Networks for Robust Image Labeling
Authors: Gary B. Huang, Viren Jain
ICLR 2014

Abstract: Feedforward multilayer networks trained by supervised learning have recently demonstrated state of the art performance on image labeling problems such as boundary prediction and scene parsing. As even very low error rates can limit practical usage of such systems, methods that perform closer to human accuracy remain desirable. In this work, we propose a new type of network with the following properties that address what we hypothesize to be limiting aspects of existing methods: (1) a `wide' structure with thousands of features, (2) a large field of view, (3) recursive iterations that exploit statistical dependencies in label space, and (4) a parallelizable architecture that can be trained in a fraction of the time compared to benchmark multilayer convolutional networks. For the specific image labeling problem of boundary prediction, we also introduce a novel example weighting algorithm that improves segmentation accuracy. Experiments in the challenging domain of connectomic reconstruction of neural circuity from 3d electron microscopy data show that these 'Deep And Wide Multiscale Recursive' (DAWMR) networks lead to new levels of image labeling performance. The highest performing architecture has twelve layers, interwoven supervised and unsupervised stages, and uses an input field of view of 157,464 voxels ($54^3$) to make a prediction at each image location. We present an associated open source software package that enables the simple and flexible creation of DAWMR networks.


Peer review:
[Review]:
This work presents an approach for 3d imaging analysis that tries to improve on previous work by addressing the limiting aspects of the existing methods. The paper presents 3 main contributions:
a fast-training wide network, a recursive approach, and a weighting scheme to pull the focus of the training towards harder cases.

This paper is well written, well structured and flows well. The results show that the methods proposed 
improve performance on the 3d image annotation task. The 3 contributions are tested and stacked one-by-one and each is shown to improve performance.

Genereal comment:
Can the improvements proposed in this paper be applied to 2d images as well? It would have been interesting to see an application to a benchmark dataset to have a comparison with current state-of-the-art methods.

Pros:
 - Well justified improvements that show clear improvements.
 - Open source software is presented.

Cons:
 - Many metrics are presented, though, as a non-expert, it is hard to judge which metrics are more relevant.