Infinite mixture prototypes for few-shot learning

Infinite mixture prototypes for few-shot learning

© 36th International Conference on Machine Learning, ICML 2019. All rights reserved. We propose infinite mixture prototypes to adaptively represent both simple and complex data distributions for few-shot learning. Infinite mixture prototypes combine deep representation learning with Bayesian nonpara...

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Bibliographic Details
Main Authors: Allen, Kelsey Rebecca, Shelhamer, Evan, Shin, Hanul, Tenenbaum, Joshua B
Other Authors: Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Format: Article
Language:English
Published: 2021
Online Access:https://hdl.handle.net/1721.1/138347.2

Internet

https://hdl.handle.net/1721.1/138347.2

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