The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
L'apprentissage incrémental, une méthodologie d'apprentissage automatique, entraîne les données d'entrée arrivant en permanence et étend les connaissances du modèle. Lorsqu’il s’agit de flux de données non étiquetés, la tâche d’apprentissage incrémentiel devient plus difficile. Notre nouvelle méthodologie d'apprentissage incrémental proposée, Data Augmented Incremental Learning (DAÏL), apprend les flux en temps réel toujours croissants avec des ressources mémoire et un temps réduits. Initialement, les lots de flux de données non étiquetés sont regroupés à l'aide de l'algorithme de clustering proposé, Clustering based on Autoencoder and Gaussian Model (CLAG). Plus tard, DAÏL crée un modèle incrémentiel mis à jour pour les clusters étiquetés à l'aide de l'augmentation des données. DAÏL évite le recyclage des anciens échantillons et conserve uniquement le modèle incrémentiel le plus récemment mis à jour contenant toutes les anciennes informations de classe. L’utilisation de l’augmentation des données dans DAÏL combine les clusters similaires générés avec différents lots de données. Une série d'expériences a vérifié les performances significatives de CLAG et DAÏL, produisant un modèle incrémental évolutif et efficace.
Sathya MADHUSUDHANAN
Sri Sivasubramaniya Nadar College of Engineering
Suresh JAGANATHAN
Sri Sivasubramaniya Nadar College of Engineering
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copier
Sathya MADHUSUDHANAN, Suresh JAGANATHAN, "Data Augmented Incremental Learning (DAIL) for Unsupervised Data" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 6, pp. 1185-1195, June 2022, doi: 10.1587/transinf.2021EDP7213.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7213/_p
Copier
@ARTICLE{e105-d_6_1185,
author={Sathya MADHUSUDHANAN, Suresh JAGANATHAN, },
journal={IEICE TRANSACTIONS on Information},
title={Data Augmented Incremental Learning (DAIL) for Unsupervised Data},
year={2022},
volume={E105-D},
number={6},
pages={1185-1195},
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.},
keywords={},
doi={10.1587/transinf.2021EDP7213},
ISSN={1745-1361},
month={June},}
Copier
TY - JOUR
TI - Data Augmented Incremental Learning (DAIL) for Unsupervised Data
T2 - IEICE TRANSACTIONS on Information
SP - 1185
EP - 1195
AU - Sathya MADHUSUDHANAN
AU - Suresh JAGANATHAN
PY - 2022
DO - 10.1587/transinf.2021EDP7213
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2022
AB - 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.
ER -