Random memory adaptation model inspired by the paper: "Memory-based parameter adaptation (MbPA)"

An implementation of the paper "Overcoming catastrophic forgetting in neural networks" (DeepMind, 2016), using Pytorch framework.

SupportNet: solving catastrophic forgetting in class incremental learning with support data

Supervised learning in PyTorch with prioritized memory replay.

A PyTorch implementation of the ECCV 2018 publication "Memory Aware Synapses: Learning what (not) to forget"

Keras-based framework for implementing continual learning methods.

Continual Learning methods using Episodic Memory (CLEM) in PyTorch

Repo for competition track Lifelong Robotic Vision, IROS 2019.

Simulation code for Limbacher, T. and Legenstein, R. (2020). Emergence of Stable Synaptic Clusters on Dendrites Through Synaptic Rewiring

This is repository contains code for experiment to evaluate catastrophic forgetting in neural networks.

Code for the paper "Incremental Learning Techniques for Semantic Segmentation", Michieli U. and Zanuttigh P., ICCVW, 2019

towards models that can gracefully forget

Implementation for the paper "SpaceNet: Make Free Space For Continual Learning" in PyTorch.

A simple experiment to compare Artificial and Spiking Neural Networks in Sequential and Few-Shot Learning.

[IWANN 2021] Reducing catastrophic forgetting in 3D point cloud objects with help of semantic information

detecting domain boundaries during inference

theoretical framework for continual learning / incremental learning

Continual Learning with Echo State Networks experiments

Code for ECML/PKDD 2020 Paper --- Continual Learning with Knowledge Transfer for Sentiment Classification