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]:
An architecture for labeling neural components in electron microscopy images is presented based on a combination of unsupervised feature learning and supervised classification.  The proposed system learns a basic feature representation using vector quantization applied to small patches.  Several strategies for combining the extracted features into a hidden representation are proposed, and these features are passed to a supervised learning stage to predict an “affinity graph” which encodes the segmentation of image components.  Subsequent stages of features are generated by computing additional features from the predicted affinity graph and the original input image in the hope of learning features that better represent the output domain.  Several modifications are proposed to improve performance, including a “hard example mining” strategy to up-weight regions of input images that are difficult to segment properly.  Results are presented on a test set, with validation numbers provided for numerous variations of the proposed architecture.  It is shown that the best system widely outperforms a baseline conv-net on many quality metrics.
 
This paper covers an interesting application domain that requires a reasonably scalable learning approach as well as some novel components to properly predict the structures desired.  In that direction, it may be interesting to other experts wanting to achieve maximum performance on this application.  

Other than the CNN baseline, it is hard to know how difficult this problem is, since the dataset is apparently novel.  On the other hand, the paper takes a “kitchen sink” approach to selecting an appropriate algorithm (as evidenced by the complex pipeline and extensive appendix with additional variations).   It is, thus, difficult to know whether the improvements are coming from these particular innovations or from some other source.  For example, in some cases it is clear that the addition of multi-scale or more layers of features reduces the training error significantly and it could be that other more orthodox architectures could achieve similar results.   What insight should a reader interested in other domains distill from these results?
 
Pros:

Interesting application involving a structured output, though similar to image segmentation.
Authors cover a wide search through a novel type of architecture to yield a successful labeler.  May be valuable to domain experts.

Cons:

A complex pipeline that combines many components [from prior art], making it hard to see what is contributed outside of this particular application.
Difficult for non-expert to judge quality of results.
 
Other comments:
The addition of dropout appears to enhance generalization (along with several of the other modifications).  This might help the CN baseline as well.  It may be useful / interesting to understand what explains the improved generalization.  (E.g., is the CN overfitting, and this avoided by the use of unsupervised training?)