Poster
Meta-Learning with Latent Embedding Optimization
Andrei A. Rusu · Dushyant Rao · Jakub Sygnowski · Oriol Vinyals · Razvan Pascanu · Simon Osindero · Raia Hadsell
Great Hall BC #28
Keywords: [ latent embedding ] [ generative ] [ hypernetworks ] [ tieredimagenet ] [ miniimagenet ] [ few-shot ] [ meta-learning ] [ optimization ]
Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems. However, they have practical difficulties when operating on high-dimensional parameter spaces in extreme low-data regimes. We show that it is possible to bypass these limitations by learning a data-dependent latent generative representation of model parameters, and performing gradient-based meta-learning in this low-dimensional latent space. The resulting approach, latent embedding optimization (LEO), decouples the gradient-based adaptation procedure from the underlying high-dimensional space of model parameters. Our evaluation shows that LEO can achieve state-of-the-art performance on the competitive miniImageNet and tieredImageNet few-shot classification tasks. Further analysis indicates LEO is able to capture uncertainty in the data, and can perform adaptation more effectively by optimizing in latent space.
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