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A model is worth tens of thousands of examples for estimation and thousands for classification
Thomas Dagès
, Laurent D. Cohen
,
Alfred M. Bruckstein
Computer Science
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Agnostic
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Amount of Training
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Custom Design
50%
Data Generation Model
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Deep Neural Network
50%
First Problem
50%
Gaussian Signals
50%
Grayscale Image
50%
Mathematical Data
100%
Mathematical Model
50%
Model-based Method
50%
Neural Network
50%
Random-dot Images
50%
Sample Complexity
50%
Signal Processing Techniques
50%
Traditional Models
50%
Training Data
50%
Transfer Learning
50%
Mathematics
Circle
100%
Deep Neural Network
100%
Gaussian Distribution
100%
Generation Model
100%
Mathematical Modeling
100%
Neural Network
100%
Signal Processing
100%
Training Data
100%
Transfer Learning
100%
Computer Science
Data Generation
50%
Deep Neural Network
50%
Generation Model
50%
Neural Network
50%
Processing Method
50%
Traditional Method
100%
Traditional Model
50%
Training Data
50%
Transfer Learning
50%