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 LEADERBOARD
Few-Shot Image Classification OMNIGLOT - 1-Shot, 5-way MAML Accuracy 98.7 # 9
Few-Shot Image Classification OMNIGLOT - 5-Shot, 5-way MAML Accuracy 99.9 # 2

Results from Other Papers


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK SOURCE PAPER COMPARE
Few-Shot Image Classification Mini-Imagenet 5-way (1-shot) MAML Accuracy 48.7 # 42
Few-Shot Image Classification Mini-Imagenet 5-way (5-shot) MAML Accuracy 63.1 # 37
Few-Shot Image Classification Mini-ImageNet-CUB 5-way (1-shot) MAML (Finn et al., 2017) Accuracy 40.15 # 6
Image Classification Tiered ImageNet 5-way (5-shot) MAML Accuracy 70.30 # 5
Image Classification Tiered ImageNet 5-way (5-shot) MAML+Transduction Accuracy 70.83 # 4

Methods used in the Paper