A PyTorch Library for Meta-learning Research
Repository for few-shot learning machine learning projects
Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow
A dataset of datasets for learning to learn from few examples
A Personalized Federated Learning (PFL-HCare) framework for IoT healthcare. Features MAML meta-learning, Differential Privacy (RDP), and gradient quantization for efficiency. Includes a React/FastAPI dashboard for real-time monitoring.
Implementations of many meta-learning algorithms to solve the few-shot learning problem in Pytorch
A PyTorch implementation of Model Agnostic Meta-Learning (MAML) that faithfully reproduces the results from the original paper.
Personalizing Dialogue Agents via Meta-Learning
Source code for KDD 2020 paper "Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation"
"모두를 위한 메타러닝" 책에 대한 코드 저장소
Meta learning with BERT as a learner
Source code for NeurIPS 2020 paper "Meta-Learning with Adaptive Hyperparameters"
Memory efficient MAML using gradient checkpointing
TensorFlow 2.0 implementation of MAML.
Tools for building raster processing and display services
[CVPR2021] Meta Batch-Instance Normalization for Generalizable Person Re-Identification
NAACL '24 (Best Demo Paper RunnerUp) / MlSys @ NeurIPS '23 - RedCoast: A Lightweight Tool to Automate Distributed Training and Inference
Official PyTorch implementation of "Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning" (ICCV2021 Oral)
A collection of Gradient-Based Meta-Learning Algorithms with pytorch
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