Teaching machines to see the process of designing (datasets) with ai

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This document summarizes a presentation about teaching machines to see and recognize images using artificial intelligence. It discusses Clarifai, a company that provides image and video recognition services using convolutional neural networks (CNNs). The presentation explains how CNNs can learn representations of images without needing manual feature engineering. It demonstrates how CNNs can be trained to detect safe versus unsafe image content. Finally, it discusses how CNNs have progressed over time to surpass traditional computer vision techniques for tasks like object detection.

Travel

Teaching Machines to See
The process of designing (datasets) with AI
Ryan Compton
ryan@clarifai.com

Clarifai
- Clarifai.com
- 40 people • $40M • Imagenet ‘13
- Image + Video Recognition
- NYC • SF

Current product applications
- Navigating and understanding internal image libraries
- Auto-proposing tags upon upload to a service
- ‘You may also like’ carousel
- Filtering user generated content

Object detection in 1996
Jain, A. K., Zhong, Y., & Lakshmanan, S. (1996). Object matching using deformable templates. IEEE Transactions on pattern
analysis and machine intelligence, 18(3), 267-278.

Learning templates in 2009
https://arxiv.org/abs/1104.2123

Disclaimer: This presentation will contain visualizations of
nudity for scientific purposes.

Eventually someone will get real good at feature
engineering and the problem will be solved, right?

convnet
Demo network for this talk (modified ZF net):
5 convolutional layers
3 fully-connected layers
2 softmax outputs (‘sfw’ and ‘nsfw’)
69,961,504 parameters

Train Test
Deploy to prod
and everything
breaks

Deconvolutional Networks
• Provides way to map activations at high
layers back to the input
• Same operations as Convnet, but in reverse:
– Unpool feature maps
– Convolve unpooled maps
• Filters copied from Convnet
– Used here purely as a probe
– Originally proposed as unsupervised learning
method
– No inference, no learning
– Compare with sparse coding Input Image
Convolution (learned)
Unpooling
Feature maps
Non-linearity
[Zeiler et al. CVPR’10, ICCV’11]

wavelets, curvelets, framelets, shapelets, shearlets...

Objective for a single layer in a
deconvnet

Reversible Max Pooling
Pooled
Feature
Maps
Max
Locations
“Switches”
Pooling Unpooling
Feature Map
Reconstructed Feature Map

First Layer
Image from Zeiler, Matthew D., and Rob Fergus. "Visualizing and understanding convolutional networks." European
Conference on Computer Vision. Springer International Publishing, 2014.

DEVELOPERS
10,000
Free API Credits
Get started now!
developer.clarifai.com

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Convnets can learn to detect nudity from large datasets of safe (SFW) images during training, even without being directly shown examples of nudity (NSFW images). When applied to new images, the convnet has learned rich semantic features that allow it to infer whether an image contains nudity or not. By using deconvolutional networks and occlusion experiments, researchers can see what patterns in images cause high activations at different layers of the convnet, helping understand what it has learned to look for. Context matters in these predictions, and customizing convnets for specific applications is important.

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Teaching machines to see the process of designing (datasets) with ai

1. Teaching Machines to See The process of designing (datasets) with AI Ryan Compton ryan@clarifai.com

2. Clarifai - Clarifai.com - 40 people • $40M • Imagenet ‘13 - Image + Video Recognition - NYC • SF

3. Current product applications - Navigating and understanding internal image libraries - Auto-proposing tags upon upload to a service - ‘You may also like’ carousel - Filtering user generated content

4. Framework - mysterious but not magical

5. Demo

6. Easy to use API

7. How simple is it to Search?

8. How simple is it to Train?

9. So why is it so easy ?

10. Representation learning

11. Learned Representations

12. Learned Representations

13. Without convnets?

14. Object detection in 1978

15. Object detection in 1996 Jain, A. K., Zhong, Y., & Lakshmanan, S. (1996). Object matching using deformable templates. IEEE Transactions on pattern analysis and machine intelligence, 18(3), 267-278.

16. Learning templates in 2009 https://arxiv.org/abs/1104.2123

17. Disclaimer: This presentation will contain visualizations of nudity for scientific purposes.

18. 1996

19.

20.

21.

22.

23.

24.

25. Eventually someone will get real good at feature engineering and the problem will be solved, right?

26. Fast forward: CNNs

27.

28. But how ?

29. SFW training data

30. NSFW training data

31. convnet Demo network for this talk (modified ZF net): 5 convolutional layers 3 fully-connected layers 2 softmax outputs (‘sfw’ and ‘nsfw’) 69,961,504 parameters

32. Train Test

33. Train Test Publish

34. Train Test Deploy to prod and everything breaks

35. And how does it do that ?

36. Occlusion Experiment

37. Occlusion Experiment

38. Occlusion Experiment

39. Deconvolutional Networks • Provides way to map activations at high layers back to the input • Same operations as Convnet, but in reverse: – Unpool feature maps – Convolve unpooled maps • Filters copied from Convnet – Used here purely as a probe – Originally proposed as unsupervised learning method – No inference, no learning – Compare with sparse coding Input Image Convolution (learned) Unpooling Feature maps Non-linearity [Zeiler et al. CVPR’10, ICCV’11]

40. Sparse Coding

41. wavelets, curvelets, framelets, shapelets, shearlets...

42. Objective for a single layer in a deconvnet

43.

44. Reuse Feedforward Switches

45. Reversible Max Pooling Pooled Feature Maps Max Locations “Switches” Pooling Unpooling Feature Map Reconstructed Feature Map

46. 2014

47. First Layer Image from Zeiler, Matthew D., and Rob Fergus. "Visualizing and understanding convolutional networks." European Conference on Computer Vision. Springer International Publishing, 2014.

48. Second Layer

49. Third Layer

50. Fourth Layer

51. Fifth Layer

52. conv1

53. conv2

54. conv3

55. conv4

56. conv5

57. fc8

58.

59. Oh and safe stuff too...