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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.
Continual learning baselines and strategies from popular papers, using Avalanche. We include EWC, SI, GEM, AGEM, LwF, iCarl, GDumb, and other strategies.
Replication of existing baselines that address incremental learning issues and definition of new approaches to overcome existing limitations
The main goal of this project is to implement a neural network capable of learning different tasks without forgetting the ones it has learned before.
A working PyTorch implementation of the iCaRL algorithm (for Cifar100 experiments)
Class Incremental Learning (iCaRL, EEIL, BiC) reproduce github repository.
A repository consists of incremental learning algorithm such as LwF and iCaRL on CIFAR100 dataset on ResNet architecture.
Implementation of several variations of the iCaRL incremental learning algorithm in PyTorch.
Reproduction of existing baselines that address incremental learning issues and definition of new approaches to overcome existing limitations