paper

Pillar Networks++: Distributed non-parametric deep and wide networks

arXiv:1708.06250

Abstract

In recent work, it was shown that combining multi-kernel based support vector machines (SVMs) can lead to near state-of-the-art performance on an action recognition dataset (HMDB-51 dataset). This was 0.4\% lower than frameworks that used hand-crafted features in addition to the deep convolutional feature extractors. In the present work, we show that combining distributed Gaussian Processes with multi-stream deep convolutional neural networks (CNN) alleviate the need to augment a neural network with hand-crafted features. In contrast to prior work, we treat each deep neural convolutional network as an expert wherein the individual predictions (and their respective uncertainties) are combined into a Product of Experts (PoE) framework.

arXiv admin note: substantial text overlap with arXiv:1707.06923

References in corpus (5)

Pillar Networks++: Distributed non-parametric deep and wide networks · wovepaper