ewc

Continual learning baselines and strategies from popular papers, using Avalanche. We include EWC, SI, GEM, AGEM, LwF, iCarl, GDumb, and other strategies.

Official Pytorch implementations of MRN: Multiplexed Routing Network for Incremental Multilingual Text Recognition (ICCV 2023)

DEPRECATED & OBSOLETE! Previously StackStorm Enterprise (EWC) Workflow Editor. Now integrated directly into StackStorm OSS Core platform (st2web).

Keras-based framework for implementing continual learning methods.

Implementation of ews weight constraint mentioned in recent Deep Mind paper: http://www.pnas.org/content/early/2017/03/13/1611835114.full.pdf

comparative evaluation of incremental machine learning methods

Tensorflow 1.x implementation of EWC, evaluated on permuted MNIST

This respository hosts the Trust List for the EWC Large Scale Pilots and is co-founded by EU Commission

A spaCy library for Named Entity Recognition with Elastic Weight Consolidation.

Experimental study of causal and Bayesian approaches to continual learning

European Waste Codes

A 23.6M-parameter LLM trained from scratch on a developmental Italian curriculum — phonemes, then words, then sentences. Benchmarks experience replay against online EWC on catastrophic forgetting.

A short script to search for optimal values of lambda in the sequential learning technique Elastic Weight Consolidation

Continual Reinforcement Learning for Adaptive Ocean Thermal Energy Conversion Control using PPO and Elastic Weight Consolidation

Login y registro creado mediante Node y EJS, el diseño es simple debido a que el enfoque va mas al sistema de logeo y sus permisos

Federated continual learning system for planetary climate forecasting. Combines FedAvg, Elastic Weight Consolidation, Physics-Informed Neural Networks, and Multi-Agent PPO across 3 geographic nodes. 29 tests · CI/CD · PyTorch

StackStorm pack containing demo workflows and automations

A continual learning thesis project evaluating pre-trained ViT-Tiny with adapters and EWC for reducing catastrophic forgetting in sequential image classification.

A comparative evaluation of continual learning strategies to mitigate catastrophic forgetting in neural networks.

A continual learning architecture that restructures itself while it learns. Beats EWC on Split MNIST; honest results on Split CIFAR 10 too.