An Open-Ended Continual Learning for Food Recognition Using Class Incremental Extreme Learning Machines

An Open-Ended Continual Learning for Food Recognition Using Class Incremental Extreme Learning Machines

State-of-the-art deep learning models for food recognition do not allow data incremental learning and often suffer from catastrophic interference problems during the class incremental learning. This is an important issue in food recognition since real-world food datasets are open-ended and dynamic,...

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Bibliographic Details
Main Authors: Ghalib Ahmed Tahir, Chu Kiong Loo
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Food recognition
deep learning
open-ended continual learning
class incremental extreme learning machine
adaptive class incremental extreme learning machine
adaptive reduced class incremental kernel extreme learning machine
Online Access:https://ieeexplore.ieee.org/document/9084095/

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