Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Learn2learn | 2,283 | 1 | 10 months ago | 19 | February 10, 2022 | 13 | mit | Python | ||
A PyTorch Library for Meta-learning Research | ||||||||||
Pytorch Meta | 1,724 | a year ago | 28 | September 20, 2021 | 53 | mit | Python | |||
A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch | ||||||||||
Learningtocompare_fsl | 891 | 5 years ago | 20 | mit | Python | |||||
PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part) | ||||||||||
Libfewshot | 771 | 3 months ago | 1 | mit | Python | |||||
LibFewShot: A Comprehensive Library for Few-shot Learning. TPAMI 2023. | ||||||||||
Easy Few Shot Learning | 737 | 5 months ago | 10 | September 25, 2023 | 5 | mit | Python | |||
Ready-to-use code and tutorial notebooks to boost your way into few-shot learning for image classification. | ||||||||||
Awesome Automl And Lightweight Models | 647 | 4 years ago | ||||||||
A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering. | ||||||||||
Pytorch Maml Rl | 645 | 2 years ago | 20 | mit | Python | |||||
Reinforcement Learning with Model-Agnostic Meta-Learning in Pytorch | ||||||||||
Few Shot | 520 | 4 years ago | 19 | mit | Python | |||||
Repository for few-shot learning machine learning projects | ||||||||||
Torchopt | 460 | 2 | 4 months ago | 14 | November 10, 2023 | 6 | apache-2.0 | Python | ||
TorchOpt is an efficient library for differentiable optimization built upon PyTorch. | ||||||||||
Pykale | 415 | 3 months ago | 12 | April 12, 2022 | 9 | mit | Python | |||
Knowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for interdisciplinary research, part of the 🔥PyTorch ecosystem. ⭐ Star to support our work! |