论文标题
连续卷积可训练的过滤器，用于建模非结构化数据
A Continuous Convolutional Trainable Filter for Modelling Unstructured Data
论文作者
论文摘要
卷积神经网络（CNN）是深度学习中最重要的架构之一。 CNN的基本构建块是可训练的过滤器，表示为离散网格，用于对离散输入数据进行卷积。在这项工作中，我们提出了一个可训练的卷积过滤器的连续版本，也可以与非结构化数据一起使用。这个新框架允许探索超出离散域的CNN，从而扩大了这种重要的学习技术的使用，以解决许多更复杂的问题。我们的实验表明，连续过滤器可以达到与最新离散过滤器相当的准确性水平，并且可以在当前的深度学习体系结构中用作解决非结构化域的问题的基础。
Convolutional Neural Network (CNN) is one of the most important architectures in deep learning. The fundamental building block of a CNN is a trainable filter, represented as a discrete grid, used to perform convolution on discrete input data. In this work, we propose a continuous version of a trainable convolutional filter able to work also with unstructured data. This new framework allows exploring CNNs beyond discrete domains, enlarging the usage of this important learning technique for many more complex problems. Our experiments show that the continuous filter can achieve a level of accuracy comparable to the state-of-the-art discrete filter, and that it can be used in current deep learning architectures as a building block to solve problems with unstructured domains as well.
