Paper Reading AI Learner

Brain-Inspired Model for Incremental Learning Using a Few Examples

2020-02-27 19:52:42
Ali Ayub, Alan Wagner

Abstract

Incremental learning attempts to develop a classifier which learns continuously from a stream of data segregated into different classes. Deep learning approaches suffer from catastrophic forgetting when learning classes incrementally. We propose a novel approach to incremental learning inspired by the concept learning model of the hippocampus that represents each image class as centroids and does not suffer from catastrophic forgetting. Classification of a test image is accomplished using the distance of the test image to the n closest centroids. We further demonstrate that our approach can incrementally learn from only a few examples per class. Evaluations of our approach on three class-incremental learning benchmarks: Caltech-101, CUBS-200-2011 and CIFAR-100 for incremental and few-shot incremental learning depict state-of-the-art results in terms of classification accuracy over all learned classes.

Abstract (translated)

URL

https://arxiv.org/abs/2002.12411

PDF

https://arxiv.org/pdf/2002.12411.pdf


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