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Show Me the Monet

Demystifying Deep Learning & AI Lightning talk on classifying monets with a CNN

SamBozek
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Show Me the Monet

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  1. Show Me the Monet Samuel E Bozek

  2. Why Monet? • Impressionism was a Driving Force in Art • Leading Artist in the Movement • Wide Subject Matter, Landscapes, Portraits, Cityscapes • Difference in Styles from Early to Late Work

  3. Model Structure

  4. Classifying Based on Monet’s Phases

  5. A Little CNN Game • Following are Three Monet Images. • Look To See Key Features • Will Then Show How Model Predicted The Input Images

  6. Most/Least Monet Paintings • Predicted Probabilities of Hold Out Set • Gives Overview of What Model Perceives as Monet/Not Monet • For Both Monet Images and Not Monet Images

  7. 99.65% Probability of Being Monet Atkinson Grimshaw London Bridge at Half Tide

  8. 99.00% Probability of Being Monet Claude Monet Farmyard in Normandy 1863

  9. 0.5% Probability of Being Monet Jan Sluyther Portrait of a Dancer

  10. 0.67% Probability of Being Monet Claude Monet The Grand Creuse at Pont de Vervy 1889

  11. 13.29% Chance of Being Monet

  12. Beyond Classification • During my initial classification work came across ANeural Algorithm of Artistic Style • Art historian senses tingling. • Programming senses nervous. • Chemist senses ambivalent.

  13. Beyond Classification • The only math I’ll throw in: • ℒtotal(p, a, x) = 훼ℒcontent(p, x) + βℒstyle(a, x) • Using Structure Outlined in Paper Created a 3 layer style transfer network to

  14. Sunrise

  15. Pre-Beard Transformation

  16. Sources • A Neural Algorithm of Artistic Style: https://arxiv.org/abs/ 1508.06576 • More Sophisticated Style Transfer: https://github.com/ fchollet/keras/blob/master/examples/ neural_style_transfer.py

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