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19 April 2024 |
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Article overview
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Meta-Curvature | | Eunbyung Park
; Junier B. Oliva
; | | Date: |
9 Feb 2019 | | Abstract: | We propose to learn curvature information for better generalization and fast
model adaptation, called meta-curvature. Based on the model-agnostic
meta-learner (MAML), we learn to transform the gradients in the inner
optimization such that the transformed gradients achieve better generalization
performance to a new task. For training large scale neural networks, we
decompose the curvature matrix into smaller matrices and capture the
dependencies of the model’s parameters with a series of tensor products. We
demonstrate the effects of our proposed method on both few-shot image
classification and few-shot reinforcement learning tasks. Experimental results
show consistent improvements on classification tasks and promising results on
reinforcement learning tasks. Furthermore, we observe faster convergence rates
of the meta-training process. Finally, we present an analysis that explains
better generalization performance with the meta-trained curvature. | | Source: | arXiv, 1902.3356 | | Services: | Forum | Review | PDF | Favorites | |
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