Concepedia

Concept

continual learning (lifelong deep learning)

Parents

359

Publications

23K

Citations

1.2K

Authors

350

Institutions

About

Continual learning (lifelong deep learning) is a machine learning paradigm within deep learning that focuses on enabling a model to learn sequentially from a stream of data or tasks arriving over time, while retaining knowledge acquired from previous data or tasks. The central objective is to prevent catastrophic forgetting, the phenomenon where learning new information causes the model to forget previously learned information. This approach aims to develop models capable of accumulating knowledge and improving performance across a lifetime of experiences, contrasting with the traditional static training paradigm on a fixed dataset. It is significant for developing adaptive systems that can operate effectively in dynamic environments without requiring complete retraining on the entire historical dataset.

Top Authors

Rankings shown are based on concept H-Index.

BL

China University of Mining and Technology

VL

University of Bologna

RP

Google (United States)

DM

University of Bologna

MF

Georgia Institute of Technology

Top Institutions

Rankings shown are based on concept H-Index.

Google (United States)

Mountain View, United States

Peking University

Beijing, China

University of Illinois Chicago

Chicago, United States