Efficient Neural Network based Classification and Outlier Detection for
  Image Moderation using Compressed Sensing and Group Testing
Sabyasachi Ghosh Sanyam Saxena Ajit Rajwade
Popular social media platforms employ neural network based image moderation
engines to classify images uploaded on them as having potentially objectionable
content. Such moderation engines must answer a large number of queries with
heavy computational cost, even though the actual number of images with
objectionable content is usually a tiny fraction. Inspired by recent work on
Neural Group Testing, we propose an approach which exploits this fact to reduce
the overall computational cost of such engines using the technique of
Compressed Sensing (CS). We present the quantitative matrix-pooled neural
network (QMPNN), which takes as input $n$ images, and a $m \times n$ binary
pooling matrix with $m < n$, whose rows indicate $m$ pools of images i.e.
selections of $r$ images out of $n$. The QMPNN efficiently outputs the product
of this matrix with the unknown sparse binary vector indicating whether each
image is objectionable or not, i.e. it outputs the number of objectionable
images in each pool. For suitable matrices, this is decoded using CS decoding
algorithms to predict which images were objectionable. The computational cost
of running the QMPNN and the CS algorithms is significantly lower than the cost
of using a neural network with the same number of parameters separately on each
image to classify the images, which we demonstrate via extensive experiments.
Our technique is inherently resilient to moderate levels of errors in the
prediction from the QMPNN. Furthermore, we present pooled deep outlier
detection, which brings CS and group testing techniques to deep outlier
detection, to provide for the case when the objectionable images do not belong
to a set of pre-defined classes. This technique enables efficient automated
moderation of off-topic images shared on topical forums dedicated to sharing
images of a certain single class, many of which are currently human-moderated.