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


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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

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Paper:
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
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Code:
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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.

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