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

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Li Fei fei, Rob Fergus, Pietro Perona

TechnologieFormation
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Slides Credit: Gary Bradski, Sebastian Thrun, Rob Fergus, Pietro Perona, Andrew Zisserman, Li Fei-Fei, Antonio Torralba
This guy is wearing a haircut called a “Mullet”
Find the Mullets… One-Shot Learning
~10,000 to 30,000
One-Shot Learning ,[object Object],L. Fei-Fei, R. Fergus and P. Perona, “A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories” ICCV 03.
 
 
 
 
 
Incorporating prior knowledge ,[object Object],[object Object]
Posterior (Same family as Prior) Likelihood Prior (conjugate to the likelihood)
 
 
 
 
 
 
 
 
 
 
Performance Results – Face Model 1 training image 5 training images
 
Performance Results – Motorbikes 1 training image 5 training images
 
 
Prior Hyper-Parameters
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
And another Comparison..
 

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