Adithya-MN / tensorflow-maml

TensorFlow 2.0 implementation of MAML.

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

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”.

MAML | Neural Net

alt-text-1 alt-text-2

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.


image.png

About

TensorFlow 2.0 implementation of MAML.

License:Apache License 2.0


Languages

Language:Jupyter Notebook 100.0%