Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

ICML 2017 Chelsea FinnPieter AbbeelSergey 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... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK COMPARE
Few-Shot Image Classification Mini-ImageNet - 1-Shot Learning MAML Accuracy 48.70% # 26
Few-Shot Image Classification Mini-ImageNet - 5-Shot Learning MAML Accuracy 63.10% # 21
Few-Shot Image Classification OMNIGLOT - 1-Shot Learning MAML Accuracy 98.7% # 4
Few-Shot Image Classification OMNIGLOT - 5-Shot Learning MAML Accuracy 99.9% # 1