Anton Lee's repositories
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).
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)
CapyMOA
Enhanced machine learning library tailored for data streams, featuring a Python API integrated with MOA backend support. This unique combination empowers users to leverage a wide array of existing algorithms efficiently while fostering the development of new methodologies in both Python and Java.
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.
efficient-kan
An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).
jellyfin
The Free Software Media System
jellyfin-web
Web Client for Jellyfin
jpype
JPype is cross language bridge to allow Python programs full access to Java class libraries.
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.
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.