🌊 Online machine learning in Python
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Updated
May 22, 2024 - Python
🌊 Online machine learning in Python
Train, Evaluate, Optimize, Deploy Computer Vision Models via OpenVINO™
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.
(CVPR 2021 Oral) Open World Object Detection
PyCIL: A Python Toolbox for Class-Incremental Learning
Framework for Analysis of Class-Incremental Learning with 12 state-of-the-art methods and 3 baselines.
Evaluate three types of task shifting with popular continual learning algorithms.
The efficient SMT-based context-bounded model checker (ESBMC)
A brain-inspired version of generative replay for continual learning with deep neural networks (e.g., class-incremental learning on CIFAR-100; PyTorch code).
A collection of incremental learning paper implementations including PODNet (ECCV20) and Ghost (CVPR-W21).
Repo that relates to the Medium blog 'Keeping your ML model in shape with Kafka, Airflow' and MLFlow'
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).
An Incremental Learning, Continual Learning, and Life-Long Learning Repository
A clean and simple data loading library for Continual Learning
The Tornado 🌪️ framework, designed and implemented for adaptive online learning and data stream mining in Python.
This is the formal code implementation of the CVPR 2022 paper 'Federated Class Incremental Learning'.
CVPR 2020 Continual Learning Challenge - Submit your CL algorithm today!
Pytorch implementation of ACCV18 paper "Revisiting Distillation and Incremental Classifier Learning."
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.
PyTorch Implementation of Learning to Prompt (L2P) for Continual Learning @ CVPR22
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