# tensorflow-maml

**Repository Path**: wangzn_2540/tensorflow-maml

## Basic Information

- **Project Name**: tensorflow-maml
- **Description**: TensorFlow 2.0 implementation of MAML.
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2021-12-28
- **Last Updated**: 2021-12-28

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Reproduction of MAML using TensorFlow 2.0.

This reproduction is highly influenced by the pytorch reproduction by Adrien Lucas Effot available at [Paper repro: Deep Metalearning using “MAML” and “Reptile”](https://towardsdatascience.com/paper-repro-deep-metalearning-using-maml-and-reptile-fd1df1cc81b0).

**MAML**   |   **Neural Net**

![alt-text-1](imgs/maml.png "MAML") ![alt-text-2](imgs/nn.png "Neural Net")

<p align="center">
  <img width="350" height="250" src="imgs/maml_vs_nn.png">
</p>

## MAML paper

https://arxiv.org/abs/1703.03400

**Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks**
*Chelsea Finn, Pieter Abbeel, Sergey Levine*

> We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on two few-shot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies.

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![image.png](https://cdn-images-1.medium.com/max/1600/1*EUt0H5AOEFkERg-OzfCC7A.png)