Uncertainty-guided Continual Learning with Bayesian Neural Networks
Type
Conference PaperAuthors
Ebrahimi, SaynaElhoseiny, Mohamed
Darrell, Trevor
Rohrbach, Marcus
KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionPreprint Posting Date
2019-06-06Date
2020-03-11Submitted Date
2019-09-25Abstract
Continual learning aims to learn new tasks without forgetting previously learned ones. This is especially challenging when one cannot access data from previous tasks and when the model has a fixed capacity. Current regularization-based continual learning algorithms need an external representation and extra computation to measure the parameters'Acknowledgements
Work done while at Facebook AI Research.Publisher
ICLRConference/Event Name
International Conference on Learning Representations (ICLR) 2020arXiv
1906.02425Additional Links
https://arxiv.org/pdf/1906.02425https://openreview.net/forum?id=HklUCCVKDB
Relations
Is Supplemented By:- [Software]
Title: SaynaEbrahimi/UCB: Original PyTorch implementation of Uncertainty-guided Continual Learning with Bayesian Neural Networks, ICLR 2020. Publication Date: 2019-02-04. github: SaynaEbrahimi/UCB Handle: 10754/667505