Meta Learning / Learning to Learn / One Shot Learning / Few Shot Learning deep-learningone-shot-learningmeta-learningfew-shot-learninglearning-to-learn UpdatedNov 26, 2018 tristandeleu/pytorch-meta Star2k A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTor...
Pytorch Code:dragen1860/MAML-Pytorch 或katerakelly/pytorch-maml Tensorflow Code:cbfinn/maml Berkeley的 Finn 在meta-learning领域创造了很多开创性的成果,尤其是在meta-reinforcement learning in robotics方面,以后会详细讲解这方面的成果。 Model-Agnostic Meta-Learning (Finn, et al. 2017) 是一个非常通用的opt...
GitHub - yhqjohn/MetaNN: MetaModule provides extensions of PyTorch Module for meta learninggithu...
scikit-learn hyperparameter-optimization bayesian-optimization hyperparameter-tuning automl automated-machine-learning smac meta-learning hyperparameter-search metalearning Updated Nov 15, 2024 Python learnables / learn2learn Star 2.7k Code Issues Pull requests A PyTorch Library for Meta-learning Resear...
image.png 改为0以后再训练就成功啦!!! image.png 参考https://github.com/tristandeleu/pytorch-meta
maml pytorch代码:https://github.com/dragen1860/MAML-Pytorch/blob/master/meta.py 代码里的实现,对每个任务,先初始化参数,对初始化的模型参数进行训练得到第一次参数,在第一次参数的更新方向上更新了初始参数。也就是第一次参数的更新决定了更新方向,第二次更新更新了实际参数。
The video tutorial can be found from:Model Agnostic Meta Learning Related Videos:My talk for Model Agnostic Meta Learning with domain adaptation Paper:https://arxiv.org/pdf/1703.03400.pdf pyTorch Implementation: 1.https://github.com/dragen1860/MAML-Pytorch ...
论文:《MAML: ModelAgnostic Meta Learning》 论文地址:https://arxiv.org/pdf/1703.03400.pdf github:httpshttps://github.com/dragen1860/MAML-Pytorch 一、算法原理 将数据集分成meta-train和meta-test两部分,meta-test测试模型的收敛速度(用Dtrain训练,用Dtest测试分类效果),meta-train用于训练模型(Dtrain和Dte...
除了用随机数,也可以用预训练的网络参数来初始化神经网络,也就是所谓 transfer learning,或者更准确地说是 fine-tuning 的技术。如果不了解 fine-tuning 技术,可以阅读这篇博客: Building powerful image classification models using very little data, 地址链接...
On software packages for meta-learningA lot of research code releases (code is fragile and sometimes broken) A few notable libraries that implement a few specific methods: Torchmeta (https://github.com/tristandeleu/pytorch-meta) Learn2learn (https://github.com/learnables/learn2learn) ...