Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow
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Updated
Sep 19, 2021 - Jupyter Notebook
Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow
A PyTorch Library for Meta-learning Research
Repository for few-shot learning machine learning projects
A dataset of datasets for learning to learn from few examples
Source code for KDD 2020 paper "Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation"
A PyTorch implementation of Model Agnostic Meta-Learning (MAML) that faithfully reproduces the results from the original paper.
Implementations of many meta-learning algorithms to solve the few-shot learning problem in Pytorch
Tools for building raster processing and display services
Personalizing Dialogue Agents via Meta-Learning
"모두를 위한 메타러닝" 책에 대한 코드 저장소
TensorFlow 2.0 implementation of MAML.
Meta learning with BERT as a learner
My notes and assignment solutions for Stanford CS330 (Fall 2019 & 2020) Deep Multi-Task and Meta Learning
[CVPR2021] Meta Batch-Instance Normalization for Generalizable Person Re-Identification
Official PyTorch implementation of "Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning" (ICCV2021 Oral)
Source code for NeurIPS 2020 paper "Meta-Learning with Adaptive Hyperparameters"
This repository implements the paper, Model-Agnostic Meta-Leanring for Fast Adaptation of Deep Networks.
Meta-Learning for EEG, Sleep Staging, Transfer Learning, Pre-trained EEG, PSG datasets (IEEE Journal of Biomedical and Health Informatics)
A collection of Gradient-Based Meta-Learning Algorithms with pytorch
PyTorch implementation of "How to Train Your MAML to Excel in Few-Shot Classification"
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