Dropout is a special case of the stochastic delta rule: faster and more accurate deep learning

Multi-layer neural networks have lead to remarkable performance on many kinds of benchmark tasks in text, speech and image processing. Nonlinear parameter estimation in hierarchical models is known to be subject to overfitting. One approach to this overfitting and related problems (local minima, colinearity, feature discovery etc.) is called dropout (Srivastava, et al 2014, Baldi et al 2016). This method removes hidden units with a Bernoulli random variable with probability $p$ over updates. In this paper we will show that Dropout is a special case of a more general model published originally in 1990 called the stochastic delta rule ( SDR, Hanson, 1990). SDR parameterizes each weight in the network as a random variable with mean $\mu_{w_{ij}}$ and standard deviation $\sigma_{w_{ij}}$. These random variables are sampled on each forward activation, consequently creating an exponential number of potential networks with shared weights. Both parameters are updated according to prediction error, thus implementing weight noise injections that reflect a local history of prediction error and efficient model averaging. SDR therefore implements a local gradient-dependent simulated annealing per weight converging to a bayes optimal network. Tests on standard benchmarks (CIFAR) using a modified version of DenseNet shows the SDR outperforms standard dropout in error by over 50% and in loss by over 50%. Furthermore, the SDR implementation converges on a solution much faster, reaching a training error of 5 in just 15 epochs with DenseNet-40 compared to standard DenseNet-40's 94 epochs.

A Study of Actor and Action Semantic Retention in Video Supervoxel Segmentation

Existing methods in the semantic computer vision community seem unable to deal with the explosion and richness of modern, open-source and social video content. Although sophisticated methods such as object detection or bag-of-words models have been well studied, they typically operate on low level features and ultimately suffer from either scalability issues or a lack of semantic meaning. On the other hand, video supervoxel segmentation has recently been established and applied to large scale data processing, which potentially serves as an intermediate representation to high level video semantic extraction. The supervoxels are rich decompositions of the video content: they capture object shape and motion well. However, it is not yet known if the supervoxel segmentation retains the semantics of the underlying video content. In this paper, we conduct a systematic study of how well the actor and action semantics are retained in video supervoxel segmentation. Our study has human observers watching supervoxel segmentation videos and trying to discriminate both actor (human or animal) and action (one of eight everyday actions). We gather and analyze a large set of 640 human perceptions over 96 videos in 3 different supervoxel scales. Furthermore, we conduct machine recognition experiments on a feature defined on supervoxel segmentation, called supervoxel shape context, which is inspired by the higher order processes in human perception. Our ultimate findings suggest that a significant amount of semantics have been well retained in the video supervoxel segmentation and can be used for further video analysis.