A bayesian framework for unsupervised one-shot learning of object categories

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Li Fei fei, Rob Fergus, Pietro Perona

Published in: Technology, Education

A bayesian framework for unsupervised one-shot learning of object categories

  1. 2. Slides Credit: Gary Bradski, Sebastian Thrun, Rob Fergus, Pietro Perona, Andrew Zisserman, Li Fei-Fei, Antonio Torralba
  2. 3. This guy is wearing a haircut called a “Mullet”
  3. 4. Find the Mullets… One-Shot Learning
  4. 5. ~10,000 to 30,000
  5. 6. One-Shot Learning <ul><li>“ The appearance of the categories we know and … the variability in their appearance, gives us important information on what to expect in a new category” 1 </li></ul>L. Fei-Fei, R. Fergus and P. Perona, “A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories” ICCV 03.
  6. 12. Incorporating prior knowledge <ul><li>The coin example </li></ul><ul><li>Bayesian methods allow us to use a “prior” information p(θ) about the nature of objects. Given the new observations we can update our knowledge into a “posterior” p(θ|x) </li></ul>
  7. 13. Posterior (Same family as Prior) Likelihood Prior (conjugate to the likelihood)
  8. 24. Performance Results – Face Model 1 training image 5 training images
  9. 26. Performance Results – Motorbikes 1 training image 5 training images
  10. 29. Prior Hyper-Parameters
  11. 30. Results Comparison 8 –15 % < 1 min 1 ~ 5 Bayesian One-Shot 5.6 -10 % Hours 200~400 Burl, et al. Weber, et al. Fergus, et al . Error rate Learning speed # training images Algorithm
  12. 31. And another Comparison..

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