Toward Efficient Learning: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

This video explains an algorithms for meta-learning that is model-agnostic. It is compatible with any model trained with gradient descent and applicable to a variety of different learning problems

0:00 - Intro 2:29 - Human Intelligence 4:07 - The goal of this meta learning 5:56 - Model-agnostic meta learning 10:17 - Step 1 - standard learning 12:04 - Step 2 - meta learning 15:59 - Algorithm 18:25 - Experiment setup 19:54 - Omniglot data 22:17 - MiniImagenet data 23:08 - Recap

Related Video: Can Machines Learn Like Humans - In-context Learning\Meta Learning https://youtu.be/no5P_0ZYoOw

Paper: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks https://arxiv.org/abs/1703.03400

Code: https://github.com/cbfinn/maml

Abstract: 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. ... https://www.youtube.com/watch?v=tGTNplKgt6Q