论文标题
卷积蜘蛛网：从二维图像的增量学习模型
Convolutional Cobweb: A Model of Incremental Learning from 2D Images
论文作者
论文摘要
本文提出了一种新的概念形成方法，该方法支持逐步学习和预测视觉图像标签的能力。这项工作将卷积图像处理的想法（从计算机视觉研究）与一种概念形成方法相结合，该方法基于对人类如何逐步形成和使用概念的心理学研究。我们通过将其应用于MNIST数字识别任务的增量变化来实验评估这种新方法。我们将其性能与CobWeb进行了比较，CobWeb是一种不支持卷积处理的概念形成方法，以及两个随着其卷积处理的复杂性而变化的卷积神经网络。这项工作代表了将现代计算机视觉思想与经典概念形成研究统一的第一步。
This paper presents a new concept formation approach that supports the ability to incrementally learn and predict labels for visual images. This work integrates the idea of convolutional image processing, from computer vision research, with a concept formation approach that is based on psychological studies of how humans incrementally form and use concepts. We experimentally evaluate this new approach by applying it to an incremental variation of the MNIST digit recognition task. We compare its performance to Cobweb, a concept formation approach that does not support convolutional processing, as well as two convolutional neural networks that vary in the complexity of their convolutional processing. This work represents a first step towards unifying modern computer vision ideas with classical concept formation research.
