卷积神经网络
MNIST数据库
卷积码
计算机科学
人工智能
模式识别(心理学)
推论
深度学习
分割
作者
Stefan Schubert,Peer Neubert,Johannes Poschmann,Peter Pretzel
出处
期刊:IEEE Intelligent Vehicles Symposium
日期:2019-06-09
卷期号:: 653-660
被引量:11
标识
DOI:10.1109/ivs.2019.8813862
摘要
Circular Convolutional Neural Networks (CCNN) are an easy to use alternative to CNNs for input data with wrap-around structure like 360°images and multi-layer laserscans. Although circular convolutions have been used in neural networks before, a detailed description and analysis is still missing. This paper closes this gap by defining circular convolutional and circular transposed convolutional layers as the replacement of their linear counterparts, and by identifying pros and cons of applying CCNNs. We experimentally evaluate their properties using a circular MNIST classification and a Velodyne laserscanner segmentation dataset. For the latter, we replace the convolutional layers in two state-of-the-art networks with the proposed circular convolutional layers. Compared to the standard CNNs, the resulting CCNNs show improved recognition rates in image border areas. This is essential to prevent blind spots in the environmental perception. Further, we present and evaluate how weight transfer can be used to obtain a CCNN from an available, readily trained CNN. Compared to alternative approaches (e.g. input padding), our experiments show benefits of CCNNs and transfered CCNNs regarding simplicity of usage (once the layer implementations are available), performance and runtime for training and inference. Implementations for Keras with Tensorflow are provided online22Open source code for Circular Convolutional and Transposed Convolutional Layers and MNIST example in Keras with Tensorflow: http://www.tu-chemnitz.de/etit/proaut/ccnn.
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