One shot learning of simple visual concepts

Brenden M. Lake, Ruslan Salakhutdinov, Jason Gross, Joshua B. Tenenbaum

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

People can learn visual concepts from just one example, but it remains a mystery how this is accomplished. Many authors have proposed that transferred knowledge from more familiar concepts is a route to one shot learning, but what is the form of this abstract knowledge? One hypothesis is that the sharing of parts is core to one shot learning, and we evaluate this idea in the domain of handwritten characters, using a massive new dataset. These simple visual concepts have a rich internal part structure, yet they are particularly tractable for computational models. We introduce a generative model of how characters are composed from strokes, where knowledge from previous characters helps to infer the latent strokes in novel characters. The stroke model outperforms a competing state-of-the-art character model on a challenging one shot learning task, and it provides a good fit to human perceptual data.

Original languageEnglish (US)
Title of host publicationExpanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011
EditorsLaura Carlson, Christoph Hoelscher, Thomas F. Shipley
PublisherThe Cognitive Science Society
Pages2568-2573
Number of pages6
ISBN (Electronic)9780976831877
StatePublished - 2011
Event33rd Annual Meeting of the Cognitive Science Society: Expanding the Space of Cognitive Science, CogSci 2011 - Boston, United States
Duration: Jul 20 2011Jul 23 2011

Publication series

NameExpanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011

Conference

Conference33rd Annual Meeting of the Cognitive Science Society: Expanding the Space of Cognitive Science, CogSci 2011
Country/TerritoryUnited States
CityBoston
Period7/20/117/23/11

Keywords

  • Bayesian modeling
  • category learning
  • neural networks
  • transfer learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

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