State-of-the-Art Techniques in Artificial Intelligence for Continual Learning: A Review

Salami, Bukola; Haataja, Keijo; Toivanen, Pekka

doi:10.15439/2021F12

Artykuł - szczegóły

Tytuł artykułu

State-of-the-Art Techniques in Artificial Intelligence for Continual Learning: A Review

Autorzy

Salami Bukola , Haataja Keijo , Toivanen Pekka

Wybrane pełne teksty z tego czasopisma

http://annals-csis.org

Identyfikatory

DOI

10.15439/2021F12

Warianty tytułu

Konferencja

Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)

Języki publikacji

Abstrakty

Artificial neural networks are used in many state-of-the-art systems for perception, and they thrive at solving classification problems, but they lack the ability to transfer that learning to a new task. Human and animals both have the capability of acquiring knowledge and transfer them continually throughout their lifespan. This term is known as continual learning. Continual learning capabilities are important to Artificial Neural Network in the real world especially with the increasing stream of data. However, it remains a challenge to be achieved because they are prone to catastrophic forgetting. Fixing this problem is critical, so that ANN incrementally learn and improve when deployed to real life situations. In this paper, we did a taxonomy of continual learning first in human by introducing plasticity-stability dilemma and some other learning and forgetting process in the brain. We did a state-of-the-art review of three different approaches to continual learning to mitigate catastrophic forgetting.

Słowa kluczowe

Artificial Intelligence Continual Learning Catastrophic Forgetting Artificial Neural Networks Stability–Plasticity Dilemma forgetting process learning process

sztuczna inteligencja kształcenie sztuczne sieci neuronowe proces uczenia proces zapominania

Wydawca

Polskie Towarzystwo Informatyczne

Czasopismo

Annals of Computer Science and Information Systems

Rocznik

2021

Tom

Vol. 26

Strony

23--32

Opis fizyczny

Bibliogr. 51 poz., wz., tab.

Twórcy

autor

Salami Bukola

bukolas@uef.fi

School of Computing, University of Eastern Finland, Kuopio, Finland

autor

Haataja Keijo

keijo.haataja@uef.fi

School of Computing, University of Eastern Finland, Kuopio, Finland

autor

Toivanen Pekka

pekka.toivanen@uef.fi

School of Computing, University of Eastern Finland, Kuopio, Finland

Bibliografia

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Uwagi

1. Preface

2. Session: 15th International Symposium Advances in Artificial Intelligence and Applications

3. Communication Papers

Typ dokumentu

Bibliografia

Identyfikator YADDA

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