IEICE Transactions on Information and Systems
Online ISSN : 1745-1361
Print ISSN : 0916-8532
Regular Section
Data Augmented Incremental Learning (DAIL) for Unsupervised Data
Sathya MADHUSUDHANANSuresh JAGANATHAN
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2022 Volume E105.D Issue 6 Pages 1185-1195

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Abstract

Incremental Learning, a machine learning methodology, trains the continuously arriving input data and extends the model's knowledge. When it comes to unlabeled data streams, incremental learning task becomes more challenging. Our newly proposed incremental learning methodology, Data Augmented Incremental Learning (DAIL), learns the ever-increasing real-time streams with reduced memory resources and time. Initially, the unlabeled batches of data streams are clustered using the proposed clustering algorithm, Clustering based on Autoencoder and Gaussian Model (CLAG). Later, DAIL creates an updated incremental model for the labelled clusters using data augmentation. DAIL avoids the retraining of old samples and retains only the most recently updated incremental model holding all old class information. The use of data augmentation in DAIL combines the similar clusters generated with different data batches. A series of experiments verified the significant performance of CLAG and DAIL, producing scalable and efficient incremental model.

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© 2022 The Institute of Electronics, Information and Communication Engineers
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