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Working with Fashion Models - PyDataLondon 2016

Working with Fashion Models - PyDataLondon 2016

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PyDataLondon 2016 presentation

Fashion is a visual medium so it makes sense for our models of fashion to include visual features. In this presentation, I'll describe how we've build a general purpose visual fashion representation using CNNs. The network is multi-task (multiple labels per image), multi-image (multiple images per label) and it runs on multiple GPUs.

I'll visually explore what is going on inside the black box of a neural network and discover how a fashion specific model sees the world differently from generic visual models. Lastly, I'll demonstrate a multi-modal applications of the representation learned by the model.

PyDataLondon 2016 presentation

Fashion is a visual medium so it makes sense for our models of fashion to include visual features. In this presentation, I'll describe how we've build a general purpose visual fashion representation using CNNs. The network is multi-task (multiple labels per image), multi-image (multiple images per label) and it runs on multiple GPUs.

I'll visually explore what is going on inside the black box of a neural network and discover how a fashion specific model sees the world differently from generic visual models. Lastly, I'll demonstrate a multi-modal applications of the representation learned by the model.

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Working with Fashion Models - PyDataLondon 2016

  1. 1. Working with Fashion Models Eddie Bell - @ejlbell
  2. 2. Lyst
  3. 3. 3 Fashion
  4. 4. general purpose visual fashion representation Aim Build a general purpose visual fashion model
  5. 5. Deep Learning Composition and representation
  6. 6. Composition
  7. 7. Representation
  8. 8. AI must fundamentally understand the world around us and this can only be achieved if it can learn to identify and disentangle the underlying explanatory factors hidden in the observed milieu of low-level sensory data. 2014 - Representation Learning: A Review and New Perspectives. - Bengio et al.
  9. 9. Male FemaleCat Dog
  10. 10. Male FemaleCat Dog
  11. 11. Male FemaleCat Dog
  12. 12. The model
  13. 13. Male Jacket Beige / Brown Iridescent Zip
  14. 14. Training 20 epochs 5 hours per epoch 16 million parameters 4 million images
  15. 15. task accuracy all 0.80 colour 0.73 gender 0.91 type 0.97 category 0.78 subcategory 0.61 task prediction conf colour blue 0.28 gender men 0.93 type shoe 0.88 category sneaker 0.38 subcategory low-top 0.21
  16. 16. Internals
  17. 17. What can we use this for?
  18. 18. Multi-modal embeddings This elegant long black coat is perfect for pydata Positive Image Negative Image Anchor word Textual context Visual context
  19. 19. w1 w2 lexical semantic opening openings 1 0.57 flared flare 1 0.87 chic chino 2 0.18 loops belt loops 5 0.99
  20. 20. embroidered logo button down curved hem front button fastening button placket point collar breathable classic slim fit
  21. 21. Composition and Representation
  22. 22. Thanks @ejlbell

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