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Incremental Learning for Online Data Using QR Factorization on Convolutional Neural Networks

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Affiliated Author(s)
홍정호
Alternative Author(s)
Hong, Jeong Ho
Journal Title
Sensors (Basel)
ISSN
1424-8220
Issued Date
2023
Keyword
artificial intelligencecompressed sensingconvolutional neural networkdeep learningimage processingincremental learning
Abstract
Catastrophic forgetting, which means a rapid forgetting of learned representations while learning new data/samples, is one of the main problems of deep neural networks. In this paper, we propose a novel incremental learning framework that can address the forgetting problem by learning new incoming data in an online manner. We develop a new incremental learning framework that can learn extra data or new classes with less catastrophic forgetting. We adopt the hippocampal memory process to the deep neural networks by defining the effective maximum of neural activation and its boundary to represent a feature distribution. In addition, we incorporate incremental QR factorization into the deep neural networks to learn new data with both existing labels and new labels with less forgetting. The QR factorization can provide the accurate subspace prior, and incremental QR factorization can reasonably express the collaboration between new data with both existing classes and new class with less forgetting. In our framework, a set of appropriate features (i.e., nodes) provides improved representation for each class. We apply our method to the convolutional neural network (CNN) for learning Cifar-100 and Cifar-10 datasets. The experimental results show that the proposed method efficiently alleviates the stability and plasticity dilemma in the deep neural networks by providing the performance stability of a trained network while effectively learning unseen data and additional new classes.
Department
Dept. of Neurology (신경과학)
Publisher
School of Medicine (의과대학)
Type
Article
ISSN
1424-8220
DOI
10.3390/s23198117
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/45302
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Neurology (신경과학)
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