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arff

ARFF formatted file reader in C++

avalanche

Avalanche: an End-to-End Library for Continual Learning.

brain-inspired-replay

A brain-inspired version of generative replay for continual learning with deep neural networks (e.g., class-incremental learning on CIFAR-100; PyTorch code).

catastrophic-diffusion

A minimal yet resourceful implementation of diffusion models (along with pretrained models + synthetic images for nine datasets)

compx556

My GitHub for handing in assignments for meta-heuristic algorithms

continual-learning-baselines

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

dpy-anti-spam

Ever wanted a bot to automatically deal with spammers? This is your discord.py library for it.

gothello

Gothello is a combination of the games of "Go" and "Othello"

jellyfin

The Free Software Media System

jellyfin-web

Web Client for Jellyfin

maelicious

mammoth

An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning

moa

MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.

my-models

online-stability-tune

Lee, A., Gomes, D. H. M., & Zhang, D. Y. (2022). Balancing the Stability-Plasticity Dilemma with Online Stability Tuning for Continual Learning. Proceedings of 2022 International Joint Conference on Neural Networks (IJCNN)

punter

recovery_likelihood

river

🌊 Online machine learning in Python

skmf-forever

A machine learning package for streaming data in Python. The other ancestor of River.

split-and-bridge

Split-and-Bridge strategy for comparison

surprisenet-cikm-23

SurpriseNet is a class incremental continual learning technique. It allows a neural network to learn from a stream or sequence of classes rather than a traditional static dataset. The main challenge it solves is differentiating classes that were never presented side-by-side.