One-shot learning by inverting a compositional causal process

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

Research output: Contribution to journalConference articlepeer-review

Abstract

People can learn a new visual class from just one example, yet machine learning algorithms typically require hundreds or thousands of examples to tackle the same problems. Here we present a Hierarchical Bayesian model based on compositionality and causality that can learn a wide range of natural (although simple) visual concepts, generalizing in human-like ways from just one image. We evaluated performance on a challenging one-shot classification task, where our model achieved a human-level error rate while substantially outperforming two deep learning models. We also tested the model on another conceptual task, generating new examples, by using a "visual Turing test" to show that our model produces human-like performance.

Original languageEnglish (US)
JournalAdvances in Neural Information Processing Systems
StatePublished - 2013
Event27th Annual Conference on Neural Information Processing Systems, NIPS 2013 - Lake Tahoe, NV, United States
Duration: Dec 5 2013Dec 10 2013

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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