🌊 Online machine learning in Python
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Updated
Dec 2, 2023 - Python
🌊 Online machine learning in Python
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.
Train, Evaluate, Optimize, Deploy Computer Vision Models via OpenVINO™
(CVPR 2021 Oral) Open World Object Detection
PyCIL: A Python Toolbox for Class-Incremental Learning
Evaluate three types of task shifting with popular continual learning algorithms.
Framework for Analysis of Class-Incremental Learning with 12 state-of-the-art methods and 3 baselines.
An Incremental Learning, Continual Learning, and Life-Long Learning Repository
A clean and simple data loading library for Continual Learning
A collection of incremental learning paper implementations including PODNet (ECCV20) and Ghost (CVPR-W21).
A collection of online continual learning paper implementations and tricks for computer vision in PyTorch, including our ASER(AAAI-21), SCR(CVPR21-W) and an online continual learning survey (Neurocomputing).
The efficient SMT-based context-bounded model checker (ESBMC)
Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)
A brain-inspired version of generative replay for continual learning with deep neural networks (e.g., class-incremental learning on CIFAR-100; PyTorch code).
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.
Fwumious Wabbit, fast on-line machine learning toolkit written in Rust
PyTorch Implementation of Learning to Prompt (L2P) for Continual Learning @ CVPR22
The Tornado 🌪️ framework, designed and implemented for adaptive online learning and data stream mining in Python.
Repo that relates to the Medium blog 'Keeping your ML model in shape with Kafka, Airflow' and MLFlow'
A curated list of Artificial Intelligence (AI) Research, tracks the cutting edge trending of AI research, including recommender systems, computer vision, machine learning, etc.
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