.png){kind=icon}
Return
Progressive learning: A deep learning framework for continual learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neunet.2020.05.011.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Continual learning is the ability of a learning system to solve new tasks by utilizing previously acquired knowledge from learning and performing prior tasks without having significant adverse effects on the acquired prior knowledge. Continual learning is key to advancing machine learning and artificial intelligence. Progressive learning is a deep learning framework for continual learning that comprises three procedures: curriculum, progression, and pruning. The curriculum procedure is used to actively select a task to learn from a set of candidate tasks. The progression procedure is used to grow the capacity of the model by adding new parameters that leverage parameters learned in prior tasks, while learning from data available for the new task at hand, without being susceptible to catastrophic forgetting. The pruning procedure is used to counteract the growth in the number of parameters as further tasks are learned, as well as to mitigate negative forward transfer, in which prior knowledge unrelated to the task at hand may interfere and worsen performance. Progressive learning is evaluated on a number of supervised classification tasks in the image recognition and speech recognition domains to demonstrate its advantages compared with baseline methods. It is shown that, when tasks are related, progressive learning leads to faster learning that converges to better generalization performance using a smaller number of dedicated parameters. y(C) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Continual learning
Computer vision
Deep learning
Machine learning
Neural networks
Speech recognition
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.3
Papers:
8.2K
Citations:
3.0W
Organization
No organization information available
Cited Papers
ShareSave
ShareSaveShareSaveShareSaveCONNECTIONIST MODELS OF RECOGNITION MEMORY - CONSTRAINTS IMPOSED BY LEARNING AND FORGETTING FUNCTIONS.png)
PSYCHOLOGICAL REVIEW
PSYCHOLOGICAL REVIEWIF5.8
PREAI
ShareSave
View more