Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments

Introduction

Typically, the basic goal of machine learning is to train a model to perform a task. In Meta-learning, the goal is to train a model to perform the task of training a model to perform a task. Hence in this case the term "Meta-Learning" has the exact meaning you would expect; the word "Meta" has the precise function of introducing a layer of abstraction.

The meta-learning task can be made more concrete by a simple example. Consider the CIFAR-100 classification task that we used for our data competition. We can alter this task from being a 100-class classification problem to a collection of 100 binary classification problems. The goal of Meta-Learning here is to design and train and single binary classifier that will perform well on a randomly sampled task given a limited amount of training data for that specific task. In other words, we would like to train a model of perform the following procedure.

• A task is sampled. The task is "Is X a dog?"
• A small set of labeled training data is provided to the model. The labels represent whether or not the image is a picture of a dog.
• The model uses the training data to adjust itself to the specific task of checking whether or not an image is a picture of a dog.

This example also highlights the intuition that the skill of sight is distinct and separable from the skill of knowing what a dog looks like.

In this paper, a probabilistic framework for meta learning is derived then applied to tasks involving simulated robotic spiders. This framework generalizes the typical machine learning set up using Markov Decision Processes.

Source

1. Chelsea Finn, Pieter Abbeel, Sergey Levine. "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks." arXiv preprint arXiv:1703.03400v3 (2017).