Demo 1: Single continual learning experiment ./main.py --experiment=splitMNIST --scenario=task --si This runs a single continual learning experiment: the method Synaptic Intelligence on the task-incremental learning scenario of Split MNIST using the academic continual learning setting. Information abou...
Three types of incremental learning(2022,Nature Machine Intelligence) This repository mainly supports experiments in theacademic continual learning setting, whereby a classification-based problem is split up into multiple, non-overlappingcontexts(ortasks, as they are often called) that must be learned se...
As your business processes have evolved to become more agile and demand greater responsiveness, a servicing strategy that operates as a continual process (vs. the old project-based approach) is a real asset. In the past, it was common to hear things like, “Our c...
@inproceedings{AGEM, title={Efficient Lifelong Learning with A-GEM}, author={Chaudhry, Arslan and Ranzato, Marc’Aurelio and Rohrbach, Marcus and Elhoseiny, Mohamed}, booktitle={ICLR}, year={2019} } @article{chaudhryER_2019, title={Continual Learning with Tiny Episodic Memories}, author={...
PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios. - GMvandeVen/continual-learning
Demo 1: Single continual learning experiment ./main.py --experiment=splitMNIST --scenario=task --si This runs a single continual learning experiment: the method Synaptic Intelligence on the task-incremental learning scenario of Split MNIST using the academic continual learning setting. Information abou...