https://github.com/telecombcn-dl/dlmm-2017-dcu Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.

1. Amaia Salvador
amaia.salvador@upc.edu
PhD Candidate
Universitat Politècnica de Catalunya
Visualization
Day 1 Lecture 5

2. 2

3. Visualization
● Learned weights
● Activations from data
● Representation space
● Gradient-based
● Optimization-based
● DeepDream
● Neural Style
3

4. Visualization
● Learned weights
● Activations from data
● Representation space
● Gradient-based
● Optimization-based
● DeepDream
● Neural Style
4

5. Visualize Learned Weights
AlexNet
conv1
Filters are only “interpretable” on the first layer
5

6. Visualize Learned Weights
layer 2 weights
layer 3 weights
Source: ConvnetJS
6

7. Visualization
● Learned weights
● Activations from data
● Representation space
● Gradient-based
● Optimization-based
● DeepDream
● Neural Style
7

8. Activations from data
8
conv1 conv5

9. Receptive Field
Girshick et al. Rich feature hierarchies for accurate object detection and semantic segmentation. CVPR 2014
9
“people”
“text”
Visualize the receptive field of a neuron on those images that activate it the most

10. Reminder: Receptive Field
10
Receptive field: Part of the input that is visible to a neuron. It increases as we stack more
convolutional layers (i.e. neurons in deeper layers have larger receptive fields).

11. Occlusion experiments
1. Iteratively forward the same image through the
network, occluding a different region at a time.
2. Keep track of the probability of the correct class
w.r.t. the position of the occluder
Zeiler and Fergus. Visualizing and Understanding Convolutional Networks. ECCV 2015 11

12. Occlusion experiments
12
(any arbitrary layer)
Zhou et al. Object detectors emerge in deep scene CNNs. ICLR 2015

13. Class Activation Maps
13
Class Activation Maps (CAMs): Replace FC layer after last conv with Global Average Pooling (GAP).
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. "Learning deep features for discriminative localization." CVPR 2016
densely connected weights

14. Class Activation Maps
14
Class Activation Maps (CAMs): The classifier weights define the contribution of each channel in the
previous layer
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. "Learning deep features for discriminative localization." CVPR 2016
contribution of the blue channel in
the conv layer to the prediction of
“australian terrier”

15. Class Activation Maps
15
Class Activation Maps (CAMs): Unsupervised object localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. "Learning deep features for discriminative localization." CVPR 2016

16. Visualization
● Learned weights
● Activations from data
● Representation space
● Gradient-based
● Optimization-based
● DeepDream
● Neural Style
16

17. t-SNE
Embed high dimensional data points
(i.e. feature codes) so that pairwise
distances are preserved in local
neighborhoods.
Maaten & Hinton. Visualizing High-Dimensional Data using t-SNE.
Journal of Machine Learning Research (2008).
17

18. t-SNE
Can be used with features from layer before classification
18

19. Visualization
● Learned weights
● Activations from data
● Representation space
● Gradient-based
● Optimization-based
● DeepDream
● Neural Style
19

20. Gradient-based approach
Compute the gradient of any neuron w.r.t. the image
1. Forward image up to the desired layer (e.g. conv5)
2. Set all gradients to 0
3. Set gradient for the neuron we are interested in to 1
4. Backpropagate to get reconstructed image (gradient on the image)
Visualize the part of an image that mostly activates a neuron
20

21. Gradient-based approach
1. Forward image up to the desired layer (e.g. conv5)
2. Set all gradients to 0
3. Set gradient for the neuron we are interested in to 1
4. Backpropagate to get reconstructed image (gradient on the image)
Springenberg, Dosovitskiy, et al. Striving for Simplicity: The All Convolutional Net. ICLR 2015
21

22. Gradient-based approach
Springenberg, Dosovitskiy, et al. Striving for Simplicity: The All Convolutional Net. ICLR 2015
22

23. Visualization
● Learned weights
● Activations from data
● Representation space
● Gradient-based
● Optimization-based
● DeepDream
● Neural Style
23

24. Optimization approach
Simonyan et al. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps, 2014
Obtain the image that maximizes a class score (or a neuron activation)
1. Forward random image
2. Set the gradient of the scores vector to be [0,0,0…,1,...,0,0]
3. Backprop to get gradient on the image
4. Update image (small step in the gradient direction)
5. Repeat
24

25. Optimization approach
Simonyan et al. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps, 2014 25

26. Visualization
● Learned weights
● Activations from data
● Representation space
● Gradient-based
● Optimization-based
● DeepDream
● Neural Style
26

27. DeepDream
https://github.com/google/deepdream
27

28. DeepDream
1. Forward image up to some layer (e.g. conv5)
2. Set the gradients to equal the layer activations
3. Backprop to get gradient on the image
4. Update image (small step in the gradient direction)
5. Repeat
28

29. DeepDream
1. Forward image up to some layer (e.g. conv5)
2. Set the gradients to equal the layer activations
3. Backprop to get gradient on the image
4. Update image (small step in the gradient direction)
5. Repeat
At each iteration, the image is
updated to boost all features that
activated in that layer in the
forward pass.
29

30. DeepDream
30
More examples here

32. Visualization
● Learned weights
● Activations from data
● Representation space
● Gradient-based
● Optimization-based
● DeepDream
● Neural Style
32

33. Neural Style
Style image Content image Result
33Gatys et al. Image Style Transfer Using Convolutional Neural Networks. CVPR 2016

34. Neural Style
Extract raw activations in all layers. These activations will represent the contents of the image.
34Gatys et al. Image Style Transfer Using Convolutional Neural Networks. CVPR 2016

35. Neural Style
V =
● Activations are also extracted from the style image for all layers.
● Instead of the raw activations, gram matrices (G) are computed at each layer to represent the style.
E.g. at conv5 [13x13x256], reshape to:
169
256
...
G = VT
V
The Gram matrix G gives the
correlations between filter responses.
35

36. Neural Style
match content
match style
Match gram matrices
from style image
Match activations
from content image
36Gatys et al. Image Style Transfer Using Convolutional Neural Networks. CVPR 2016

37. Neural Style
match content
match style
Match gram matrices
from style image
Match activations
from content image
37Gatys et al. Image Style Transfer Using Convolutional Neural Networks. CVPR 2016

38. Neural Style
38Gatys et al. Image Style Transfer Using Convolutional Neural Networks. CVPR 2016

39. Neural Style
39
Improvements since Gatys et al.:
Johnson et al. Perceptual Losses for Real-Time Style Transfer and Super-Resolution
ECCV 2016 [code]
● 100x faster than original method
● Needs to train a network for a small amount of time for each style to render
Luan et al. Deep Photo Style Transfer. arXiv Apr 2017 [code]
● Constraint input-output transformation to be locally affine in colorspace

40. 40
Content Image Style Image Result
Luan et al. Deep Photo Style Transfer. arXiv Apr 2017

41. 41
Content Image Style Image Result
Luan et al. Deep Photo Style Transfer. arXiv Apr 2017

42. Visualization
● Learned weights
● Activations from data
● Representation space
● Gradient-based
● Optimization-based
● DeepDream
● Neural Style
42

43. Questions ?