文章基本信息

标题：Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition
本地全文：下载
作者：Spoerer, Courtney J. ; McClure, Patrick ; Kriegeskorte, Nikolaus 等
期刊名称：Frontiers in Psychology
电子版ISSN：1664-1078
出版年度：2017
卷号：8
页码：1551-1564
DOI：10.3389/fpsyg.2017.01551
出版社：Frontiers Media
摘要：Feedforward neural networks provide the dominant model of how the brain performs visual object recognition. However, these networks lack the lateral and feedback connections, and the resulting recurrent neuronal dynamics, of the ventral visual pathway in the human and nonhuman primate brain. Here we investigate recurrent convolutional neural networks with bottom-up (B), lateral (L), and top-down (T) connections. Combining these types of connections yields four architectures (B, BT, BL, and BLT), which we systematically test and compare. We hypothesized that recurrent dynamics might improve recognition performance in the challenging scenario of partial occlusion. We introduce two novel occluded object recognition tasks to test the efficacy of the models, \emph{digit clutter} (where multiple target digits occlude one another) and \emph{digit debris} (where target digits are occluded by digit fragments). We find that recurrent neural networks outperform feedforward control models (approximately matched in parametric complexity) at recognising objects, both in the absence of occlusion and in all occlusion conditions. Recurrent networks were also found to be more robust to the inclusion of additive Gaussian noise. Recurrent neural networks are better in two respects: (1) they are more neurobiologically realistic than their feedforward counterparts; (2) they are better in terms of their ability to recognise objects, especially under challenging conditions. This work shows that computer vision can benefit from using recurrent convolutional architectures and suggests that the ubiquitous recurrent connections in biological brains are essential for task performance.
关键词：object recognition; occlusion; top; down processing; Convolutional Neural Network; recurrent neural network