Reproduction of existing baselines that address incremental learning issues and definition of new approaches to overcome existing limitations
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Updated
Aug 3, 2020 - Jupyter Notebook
Reproduction 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.
Implementation of several variations of the iCaRL incremental learning algorithm in PyTorch.
Class Incremental Learning (iCaRL, EEIL, BiC) reproduce github repository.
Replication of existing baselines that address incremental learning issues and definition of new approaches to overcome existing limitations
Continual Hyperparameter Selection Framework. Compares 11 state-of-the-art Lifelong Learning methods and 4 baselines. Official Codebase of "A continual learning survey: Defying forgetting in classification tasks." in IEEE TPAMI.
Continual learning baselines and strategies from popular papers, using Avalanche. We include EWC, SI, GEM, AGEM, LwF, iCarl, GDumb, and other strategies.
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.
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