One-shot learning by inverting a compositional causal process
Name
Tenenbaum_One-shot learning.pdf
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2.52 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Lake, Brenden M.
Salakhutdinov, Ruslan
Tenenbaum, Joshua B.
Date Issued
December 2013
Journal
Advances in Neural Information Processing Systems 26 (NIPS 2013)
Publisher
Neural Information Processing Systems Foundation, Inc.
Citation
Lake, Brenden M., Ruslan Salakhutdinov and Joshua B. Tenenbaum. "One-shot learning by inverting a compositional causal process." Advances in Neural Information Processing Systems 26, NIPS 2013, Lake Tahoe, Nevada, United States, December 5-10, 2013.
Version
Author's final manuscript
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 used a visual Turing test "to show that our model produces human-like performance on other conceptual tasks, including generating new examples and parsing."
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
http://papers.nips.cc/paper/5128-one-shot-learning-by-inverting-a-compositional-causal-process
