Convolutional Cobweb: A Model of Incremental Learning from 2D Images
This work addresses the challenge of incremental learning in computer vision for researchers, representing an incremental step by combining existing ideas from different fields.
The paper tackled the problem of incremental learning for visual images by integrating convolutional image processing with a psychological concept formation approach, achieving performance evaluated on an incremental MNIST digit recognition task compared to baseline methods.
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.