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Industry Applications for Computer Vision and Deep Learning

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Overview of deep learning work at Curalate and other cool applications in computer vision.

Presented at NY Deep Learning Meetup (April 19, 2017).

Published in: Software
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Industry Applications for Computer Vision and Deep Learning

  1. 1. Industry Applications for Computer Vision and Deep Learning Andrew Kae NY Deep Learning Meetup
  2. 2. Great time to be in Deep Learning + Computer Vision! ● Lots of publicly available data/code ○ ImageNet, Pascal VOC, Github ● Very good performance ○ ImageNet, LFW ● Several good deep learning toolkits available ○ Caffe, Torch, TensorFlow ● Cloud resources ○ AWS, Google Cloud
  3. 3. Overview ● Applications of deep learning at Curalate ● Other cool industry applications
  4. 4. Curalate
  5. 5. Curalate ● Help our clients monetize their imagery ● Help enable discovery and drive higher ROI ● We’re hiring!
  6. 6. Fanreel
  7. 7. Fanreel ● UGC (user-generated content) and editorial imagery are more engaging and drive more revenue than stock product photography ● But tagging products is slow, tedious, and error-prone
  8. 8. ● Automatically tag products ○ Cut down on workload ● Other applications ○ Show related products ○ Find cheaper versions of high-end products ○ Find complementary products Intelligent Product Tagging [blog post]
  9. 9. Intelligent Product Tagging
  10. 10. Intelligent Product Tagging
  11. 11. Intelligent Product Tagging ● Demo
  12. 12. Intelligent Product Tagging ● Detection: Find the products in a query image ● Search: Find the best match for each product ● State-of-the-art uses deep learning for both components
  13. 13. Convolutional Neural Network https://www.clarifai.com/technology
  14. 14. Detection ● R-CNN [Rich feature hierarchies for accurate object detection and semantic segmentation, CVPR 2014]
  15. 15. R-CNN performance
  16. 16. Detection ● Fast R-CNN [Fast R-CNN, ICCV 2015]
  17. 17. Detection ● Faster R-CNN [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, NIPS 2015]
  18. 18. Faster R-CNN [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, NIPS 2015]
  19. 19. Search ● Take last fully connected layer and do nearest neighbor search https://www.clarifai.com/technology
  20. 20. Metric Learning [Learning Fine-grained Image Similarity with Deep Ranking, CVPR 2014] ● Learn fine-grained similarity metric from triplets
  21. 21. Metric Learning
  22. 22. Intelligent Product Tagging ● Detection: Find the products in a query image ● Search: Find the best match for each product ● State-of-the-art uses deep learning for both components
  23. 23. Other deep learning applications at Curalate
  24. 24. Spam Detection
  25. 25. Emojini ● Predict emoji for given image ● Demo
  26. 26. Other Cool Applications
  27. 27. Applications ● Object Recognition ● Face Recognition
  28. 28. Colorful Image Colorization [Colorful Image Colorization, ECCV 2016]
  29. 29. Colorful Image Colorization [Colorful Image Colorization, ECCV 2016] ● Colorization “Turing Test”: 32% fooled ● Used in reddit’s colorizeBot
  30. 30. Colorful Image Colorization [Colorful Image Colorization, ECCV 2016]
  31. 31. Colorful Image Colorization [Colorful Image Colorization, ECCV 2016]
  32. 32. Colorful Image Colorization [Colorful Image Colorization, ECCV 2016] ● Inherent ambiguities
  33. 33. Artistic Style Transfer [Image Style Transfer Using Convolutional Neural Networks, CVPR 2016]
  34. 34. Artistic Style Transfer
  35. 35. Photorealistic Style Transfer [Deep Photo Style Transfer, CVPR 2017]
  36. 36. Photorealistic Style Transfer [Deep Photo Style Transfer, CVPR 2017]
  37. 37. Caption Generation [Show and Tell: A Neural Image Caption Generator, CVPR 2015]
  38. 38. Caption Generation [Show and Tell: A Neural Image Caption Generator, CVPR 2015]
  39. 39. Generative Adversarial Networks [NIPS 2014] “There are many interesting recent development in deep learning…The most important one, in my opinion, is adversarial training (also called GAN for Generative Adversarial Networks). This, and the variations that are now being proposed is the most interesting idea in the last 10 years in ML, in my opinion.” – Yann LeCun [link]
  40. 40. Generative Adversarial Networks [NIPS 2014] ● Most CNN models are discriminative ● GAN framework ○ Generative model ○ Discriminative model ● Analogous to counterfeiters vs police
  41. 41. Generative Adversarial Networks [NIPS 2014] https://medium.com/@ageitgey/abusing-generative-adversarial-networks-to-make-8-bit-pixel-art-e45d9b96cee7 Discriminator Generator
  42. 42. GAN samples [UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS , ICLR 2016]
  43. 43. Poke-GANs [http://bohemia.hatenablog.com/entry/2016/08/13/132314]
  44. 44. Thank you! Any questions?

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