Industry Applications for Computer Vision and Deep Learning

1. Industry Applications for Computer Vision and Deep Learning Andrew Kae NY Deep Learning Meetup

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. Overview ● Applications of deep learning at Curalate ● Other cool industry applications

4. Curalate

5. Curalate ● Help our clients monetize their imagery ● Help enable discovery and drive higher ROI ● We’re hiring!

6. Fanreel

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. ● 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. Intelligent Product Tagging

10. Intelligent Product Tagging

11. Intelligent Product Tagging ● Demo

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. Convolutional Neural Network https://www.clarifai.com/technology

14. Detection ● R-CNN [Rich feature hierarchies for accurate object detection and semantic segmentation, CVPR 2014]

15. R-CNN performance

16. Detection ● Fast R-CNN [Fast R-CNN, ICCV 2015]

17. Detection ● Faster R-CNN [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, NIPS 2015]

18. Faster R-CNN [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, NIPS 2015]

19. Search ● Take last fully connected layer and do nearest neighbor search https://www.clarifai.com/technology

20. Metric Learning [Learning Fine-grained Image Similarity with Deep Ranking, CVPR 2014] ● Learn fine-grained similarity metric from triplets

21. Metric Learning

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. Other deep learning applications at Curalate

24. Spam Detection

25. Emojini ● Predict emoji for given image ● Demo

26. Other Cool Applications

27. Applications ● Object Recognition ● Face Recognition

28. Colorful Image Colorization [Colorful Image Colorization, ECCV 2016]

29. Colorful Image Colorization [Colorful Image Colorization, ECCV 2016] ● Colorization “Turing Test”: 32% fooled ● Used in reddit’s colorizeBot

32. Colorful Image Colorization [Colorful Image Colorization, ECCV 2016] ● Inherent ambiguities

33. Artistic Style Transfer [Image Style Transfer Using Convolutional Neural Networks, CVPR 2016]

34. Artistic Style Transfer

35. Photorealistic Style Transfer [Deep Photo Style Transfer, CVPR 2017]

36. Photorealistic Style Transfer [Deep Photo Style Transfer, CVPR 2017]

37. Caption Generation [Show and Tell: A Neural Image Caption Generator, CVPR 2015]

38. Caption Generation [Show and Tell: A Neural Image Caption Generator, CVPR 2015]

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. Generative Adversarial Networks [NIPS 2014] ● Most CNN models are discriminative ● GAN framework ○ Generative model ○ Discriminative model ● Analogous to counterfeiters vs police

41. Generative Adversarial Networks [NIPS 2014] https://medium.com/@ageitgey/abusing-generative-adversarial-networks-to-make-8-bit-pixel-art-e45d9b96cee7 Discriminator Generator

42. GAN samples [UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS , ICLR 2016]

43. Poke-GANs [http://bohemia.hatenablog.com/entry/2016/08/13/132314]

44. Thank you! Any questions?

Industry Applications for Computer Vision and Deep Learning

Industry Applications for Computer Vision and Deep Learning

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