Visualizing and Understanding. Fei-Fei Li & Justin Johnson & Serena Yeung. Lecture 12 -
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1 Lecture 12: Visualizing and Understanding Lecture 12-1 May 16, 2017
2 Administrative Milestones due tonight on Canvas, 11:59pm Midterm grades released on Gradescope this week A3 due next Friday, 5/26 HyperQuest deadline extended to Sunday 5/21, 11:59pm Poster session is June 6 Lecture 11-2 May 10, 2017
3 Last Time: Lots of Computer Vision Tasks Semantic Segmentation Classification + Localization Object Detection GRASS, CAT, TREE, SKY CAT DOG, DOG, CAT No objects, just pixels Single Object This image is CC0 public domain Instance Segmentation DOG, DOG, CAT Multiple Object Lecture 11 - This image is CC0 public domain 3 May 10, 2017
4 What s going on inside ConvNets? This image is CC0 public domain Class Scores: 1000 numbers Input Image: 3 x 224 x 224 What are the intermediate features looking for? Krizhevsky et al, ImageNet Classification with Deep Convolutional Neural Networks, NIPS Figure reproduced with permission. Lecture 11-4 May 10, 2017
5 First Layer: Visualize Filters ResNet-18: 64 x 3 x 7 x 7 ResNet-101: 64 x 3 x 7 x 7 DenseNet-121: 64 x 3 x 7 x 7 AlexNet: 64 x 3 x 11 x 11 Krizhevsky, One weird trick for parallelizing convolutional neural networks, arxiv 2014 He et al, Deep Residual Learning for Image Recognition, CVPR 2016 Huang et al, Densely Connected Convolutional Networks, CVPR 2017 Lecture 11-5 May 10, 2017
6 Visualize the filters/kernels (raw weights) layer 1 weights We can visualize filters at higher layers, but not that interesting layer 2 weights 16 x 3 x 7 x 7 20 x 16 x 7 x 7 (these are taken from ConvNetJS CIFAR-10 demo) layer 3 weights 20 x 20 x 7 x 7 Lecture 11-6 May 10, 2017
7 Last Layer FC7 layer 4096-dimensional feature vector for an image (layer immediately before the classifier) Run the network on many images, collect the feature vectors Lecture 11-7 May 10, 2017
8 Last Layer: Nearest Neighbors 4096-dim vector Test image L2 Nearest neighbors in feature space Recall: Nearest neighbors in pixel space Krizhevsky et al, ImageNet Classification with Deep Convolutional Neural Networks, NIPS Figures reproduced with permission. Lecture 11-8 May 10, 2017
9 Last Layer: Dimensionality Reduction Visualize the space of FC7 feature vectors by reducing dimensionality of vectors from 4096 to 2 dimensions Simple algorithm: Principle Component Analysis (PCA) More complex: t-sne Van der Maaten and Hinton, Visualizing Data using t-sne, JMLR 2008 Figure copyright Laurens van der Maaten and Geoff Hinton, Reproduced with permission. Lecture 11-9 May 10, 2017
10 Last Layer: Dimensionality Reduction Van der Maaten and Hinton, Visualizing Data using t-sne, JMLR 2008 Krizhevsky et al, ImageNet Classification with Deep Convolutional Neural Networks, NIPS Figure reproduced with permission. See high-resolution versions at Lecture May 10, 2017
11 Visualizing Activations conv5 feature map is 128x13x13; visualize as x13 grayscale images Yosinski et al, Understanding Neural Networks Through Deep Visualization, ICML DL Workshop Figure copyright Jason Yosinski, Reproduced with permission. Lecture May 10, 2017
12 Maximally Activating Patches Pick a layer and a channel; e.g. conv5 is 128 x 13 x 13, pick channel 17/128 Run many images through the network, record values of chosen channel Visualize image patches that correspond to maximal activations Springenberg et al, Striving for Simplicity: The All Convolutional Net, ICLR Workshop 2015 Figure copyright Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, Martin Riedmiller, 2015; reproduced with permission. Lecture May 10, 2017
13 Occlusion Experiments Mask part of the image before feeding to CNN, draw heatmap of probability at each mask location Zeiler and Fergus, Visualizing and Understanding Convolutional Networks, ECCV 2014 Boat image is CC0 public domain Elephant image is CC0 public domain Go-Karts image is CC0 public domain Lecture May 10, 2017
14 Saliency Maps How to tell which pixels matter for classification? Dog Simonyan, Vedaldi, and Zisserman, Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps, ICLR Workshop Figures copyright Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman, 2014; reproduced with permission. Lecture May 10, 2017
15 Saliency Maps How to tell which pixels matter for classification? Dog Compute gradient of (unnormalized) class score with respect to image pixels, take absolute value and max over RGB channels Simonyan, Vedaldi, and Zisserman, Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps, ICLR Workshop Figures copyright Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman, 2014; reproduced with permission. Lecture May 10, 2017
16 Saliency Maps Simonyan, Vedaldi, and Zisserman, Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps, ICLR Workshop Figures copyright Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman, 2014; reproduced with permission. Lecture May 10, 2017
17 Saliency Maps: Segmentation without supervision Use GrabCut on saliency map Simonyan, Vedaldi, and Zisserman, Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps, ICLR Workshop Figures copyright Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman, 2014; reproduced with permission. Rother et al, Grabcut: Interactive foreground extraction using iterated graph cuts, ACM TOG 2004 Lecture May 10, 2017
18 Intermediate Features via (guided) backprop Pick a single intermediate neuron, e.g. one value in 128 x 13 x 13 conv5 feature map Compute gradient of neuron value with respect to image pixels Zeiler and Fergus, Visualizing and Understanding Convolutional Networks, ECCV 2014 Springenberg et al, Striving for Simplicity: The All Convolutional Net, ICLR Workshop 2015 Lecture May 10, 2017
19 Intermediate features via (guided) backprop ReLU Pick a single intermediate neuron, e.g. one value in 128 x 13 x 13 conv5 feature map Compute gradient of neuron value with respect to image pixels Zeiler and Fergus, Visualizing and Understanding Convolutional Networks, ECCV 2014 Springenberg et al, Striving for Simplicity: The All Convolutional Net, ICLR Workshop 2015 Images come out nicer if you only backprop positive gradients through each ReLU (guided backprop) Figure copyright Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, Martin Riedmiller, 2015; reproduced with permission. Lecture May 10, 2017
20 Intermediate features via (guided) backprop Zeiler and Fergus, Visualizing and Understanding Convolutional Networks, ECCV 2014 Springenberg et al, Striving for Simplicity: The All Convolutional Net, ICLR Workshop 2015 Figure copyright Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, Martin Riedmiller, 2015; reproduced with permission. Lecture May 10, 2017
21 Visualizing CNN features: Gradient Ascent (Guided) backprop: Find the part of an image that a neuron responds to Gradient ascent: Generate a synthetic image that maximally activates a neuron I* = arg maxi f(i) + R(I) Neuron value Natural image regularizer Lecture May 10, 2017
22 Visualizing CNN features: Gradient Ascent 1. Initialize image to zeros score for class c (before Softmax) zero image Repeat: 2. Forward image to compute current scores 3. Backprop to get gradient of neuron value with respect to image pixels 4. Make a small update to the image Lecture May 10, 2017
23 Visualizing CNN features: Gradient Ascent Simple regularizer: Penalize L2 norm of generated image Simonyan, Vedaldi, and Zisserman, Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps, ICLR Workshop Figures copyright Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman, 2014; reproduced with permission. Lecture May 10, 2017
24 Visualizing CNN features: Gradient Ascent Simple regularizer: Penalize L2 norm of generated image Simonyan, Vedaldi, and Zisserman, Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps, ICLR Workshop Figures copyright Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman, 2014; reproduced with permission. Lecture May 10, 2017
25 Visualizing CNN features: Gradient Ascent Simple regularizer: Penalize L2 norm of generated image Yosinski et al, Understanding Neural Networks Through Deep Visualization, ICML DL Workshop Figure copyright Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson, Reproduced with permission. Lecture May 10, 2017
26 Visualizing CNN features: Gradient Ascent Better regularizer: Penalize L2 norm of image; also during optimization periodically (1) (2) (3) Gaussian blur image Clip pixels with small values to 0 Clip pixels with small gradients to 0 Yosinski et al, Understanding Neural Networks Through Deep Visualization, ICML DL Workshop Lecture May 10, 2017
27 Visualizing CNN features: Gradient Ascent Better regularizer: Penalize L2 norm of image; also during optimization periodically (1) (2) (3) Gaussian blur image Clip pixels with small values to 0 Clip pixels with small gradients to 0 Yosinski et al, Understanding Neural Networks Through Deep Visualization, ICML DL Workshop Figure copyright Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson, Reproduced with permission. Lecture May 10, 2017
28 Visualizing CNN features: Gradient Ascent Better regularizer: Penalize L2 norm of image; also during optimization periodically (1) (2) (3) Gaussian blur image Clip pixels with small values to 0 Clip pixels with small gradients to 0 Yosinski et al, Understanding Neural Networks Through Deep Visualization, ICML DL Workshop Figure copyright Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson, Reproduced with permission. Lecture May 10, 2017
29 Visualizing CNN features: Gradient Ascent Use the same approach to visualize intermediate features Yosinski et al, Understanding Neural Networks Through Deep Visualization, ICML DL Workshop Figure copyright Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson, Reproduced with permission. Lecture May 10, 2017
30 Visualizing CNN features: Gradient Ascent Use the same approach to visualize intermediate features Yosinski et al, Understanding Neural Networks Through Deep Visualization, ICML DL Workshop Figure copyright Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson, Reproduced with permission. Lecture May 10, 2017
31 Visualizing CNN features: Gradient Ascent Adding multi-faceted visualization gives even nicer results: (Plus more careful regularization, center-bias) Nguyen et al, Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks, ICML Visualization for Deep Learning Workshop Figures copyright Anh Nguyen, Jason Yosinski, and Jeff Clune, 2016; reproduced with permission. Lecture May 10, 2017
32 Visualizing CNN features: Gradient Ascent Nguyen et al, Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks, ICML Visualization for Deep Learning Workshop Figures copyright Anh Nguyen, Jason Yosinski, and Jeff Clune, 2016; reproduced with permission. Lecture May 10, 2017
33 Visualizing CNN features: Gradient Ascent Optimize in FC6 latent space instead of pixel space: Nguyen et al, Synthesizing the preferred inputs for neurons in neural networks via deep generator networks, NIPS 2016 Figure copyright Nguyen et al, 2016; reproduced with permission. Lecture May 10, 2017
34 Fooling Images / Adversarial Examples (1) (2) (3) (4) Start from an arbitrary image Pick an arbitrary class Modify the image to maximize the class Repeat until network is fooled Lecture May 10, 2017
35 Fooling Images / Adversarial Examples Boat image is CC0 public domain Elephant image is CC0 public domain Lecture May 10, 2017
36 Fooling Images / Adversarial Examples Boat image is CC0 public domain Elephant image is CC0 public domain What is going on? Ian Goodfellow will explain Lecture May 10, 2017
37 DeepDream: Amplify existing features Rather than synthesizing an image to maximize a specific neuron, instead try to amplify the neuron activations at some layer in the network Choose an image and a layer in a CNN; repeat: 1. Forward: compute activations at chosen layer 2. Set gradient of chosen layer equal to its activation 3. Backward: Compute gradient on image 4. Update image Mordvintsev, Olah, and Tyka, Inceptionism: Going Deeper into Neural Networks, Google Research Blog. Images are licensed under CC-BY Lecture May 10, 2017
38 DeepDream: Amplify existing features Rather than synthesizing an image to maximize a specific neuron, instead try to amplify the neuron activations at some layer in the network Choose an image and a layer in a CNN; repeat: 1. Forward: compute activations at chosen layer 2. Set gradient of chosen layer equal to its activation 3. Backward: Compute gradient on image 4. Update image Equivalent to: I* = arg maxi i fi(i)2 Mordvintsev, Olah, and Tyka, Inceptionism: Going Deeper into Neural Networks, Google Research Blog. Images are licensed under CC-BY Lecture May 10, 2017
39 DeepDream: Amplify existing features Code is very simple but it uses a couple tricks: (Code is licensed under Apache 2.0) Lecture May 10, 2017
40 DeepDream: Amplify existing features Code is very simple but it uses a couple tricks: (Code is licensed under Apache 2.0) Jitter image Lecture May 10, 2017
41 DeepDream: Amplify existing features Code is very simple but it uses a couple tricks: (Code is licensed under Apache 2.0) Jitter image L1 Normalize gradients Lecture May 10, 2017
42 DeepDream: Amplify existing features Code is very simple but it uses a couple tricks: (Code is licensed under Apache 2.0) Jitter image L1 Normalize gradients Clip pixel values Also uses multiscale processing for a fractal effect (not shown) Lecture May 10, 2017
43 Sky image is licensed under CC-BY SA 3.0 Lecture May 10, 2017
44 Image is licensed under CC-BY 4.0 Lecture May 10, 2017
45 Image is licensed under CC-BY 4.0 Lecture May 10, 2017
46 Image is licensed under CC-BY 3.0 Lecture May 10, 2017
47 Image is licensed under CC-BY 3.0 Lecture May 10, 2017
48 Image is licensed under CC-BY 4.0 Lecture May 10, 2017
49 Feature Inversion Given a CNN feature vector for an image, find a new image that: - Matches the given feature vector - looks natural (image prior regularization) Given feature vector Features of new image Total Variation regularizer (encourages spatial smoothness) Mahendran and Vedaldi, Understanding Deep Image Representations by Inverting Them, CVPR 2015 Lecture May 10, 2017
50 Feature Inversion Reconstructing from different layers of VGG-16 Mahendran and Vedaldi, Understanding Deep Image Representations by Inverting Them, CVPR 2015 Figure from Johnson, Alahi, and Fei-Fei, Perceptual Losses for Real-Time Style Transfer and Super-Resolution, ECCV Copyright Springer, Reproduced for educational purposes. Lecture May 10, 2017
51 Texture Synthesis Given a sample patch of some texture, can we generate a bigger image of the same texture? Input Output Output image is licensed under the MIT license Lecture May 10, 2017
52 Texture Synthesis: Nearest Neighbor Generate pixels one at a time in scanline order; form neighborhood of already generated pixels and copy nearest neighbor from input Wei and Levoy, Fast Texture Synthesis using Tree-structured Vector Quantization, SIGGRAPH 2000 Efros and Leung, Texture Synthesis by Non-parametric Sampling, ICCV 1999 Lecture May 10, 2017
53 Texture Synthesis: Nearest Neighbor Images licensed under the MIT license Lecture May 10, 2017
54 Neural Texture Synthesis: Gram Matrix C H w This image is in the public domain. Each layer of CNN gives C x H x W tensor of features; H x W grid of C-dimensional vectors Lecture May 10, 2017
55 Neural Texture Synthesis: Gram Matrix C C H C w This image is in the public domain. Each layer of CNN gives C x H x W tensor of features; H x W grid of C-dimensional vectors Outer product of two C-dimensional vectors gives C x C matrix measuring co-occurrence Lecture May 10, 2017
56 Neural Texture Synthesis: Gram Matrix C C H C w This image is in the public domain. Each layer of CNN gives C x H x W tensor of features; H x W grid of C-dimensional vectors Gram Matrix Outer product of two C-dimensional vectors gives C x C matrix measuring co-occurrence Average over all HW pairs of vectors, giving Gram matrix of shape C x C Lecture May 10, 2017
57 Neural Texture Synthesis: Gram Matrix C C H C w This image is in the public domain. Each layer of CNN gives C x H x W tensor of features; H x W grid of C-dimensional vectors Efficient to compute; reshape features from Outer product of two C-dimensional vectors gives C x C matrix measuring co-occurrence Average over all HW pairs of vectors, giving Gram matrix of shape C x C C x H x W to =C x HW then compute G = FFT Lecture May 10, 2017
58 Neural Texture Synthesis Pretrain a CNN on ImageNet (VGG-19) Run input texture forward through CNN, record activations on every layer; layer i gives feature map of shape Ci Hi Wi At each layer compute the Gram matrix giving outer product of features: (shape Ci Ci) Initialize generated image from random noise Pass generated image through CNN, compute Gram matrix on each layer Compute loss: weighted sum of L2 distance between Gram matrices Backprop to get gradient on image Make gradient step on image GOTO 5 Gatys, Ecker, and Bethge, Texture Synthesis Using Convolutional Neural Networks, NIPS 2015 Figure copyright Leon Gatys, Alexander S. Ecker, and Matthias Bethge, Reproduced with permission. Lecture May 10, 2017
59 Neural Texture Synthesis Pretrain a CNN on ImageNet (VGG-19) Run input texture forward through CNN, record activations on every layer; layer i gives feature map of shape Ci Hi Wi At each layer compute the Gram matrix giving outer product of features: (shape Ci Ci) Initialize generated image from random noise Pass generated image through CNN, compute Gram matrix on each layer Compute loss: weighted sum of L2 distance between Gram matrices Backprop to get gradient on image Make gradient step on image GOTO 5 Gatys, Ecker, and Bethge, Texture Synthesis Using Convolutional Neural Networks, NIPS 2015 Figure copyright Leon Gatys, Alexander S. Ecker, and Matthias Bethge, Reproduced with permission. Lecture May 10, 2017
60 Neural Texture Synthesis Pretrain a CNN on ImageNet (VGG-19) Run input texture forward through CNN, record activations on every layer; layer i gives feature map of shape Ci Hi Wi At each layer compute the Gram matrix giving outer product of features: (shape Ci Ci) Initialize generated image from random noise Pass generated image through CNN, compute Gram matrix on each layer Compute loss: weighted sum of L2 distance between Gram matrices Backprop to get gradient on image Make gradient step on image GOTO 5 Gatys, Ecker, and Bethge, Texture Synthesis Using Convolutional Neural Networks, NIPS 2015 Figure copyright Leon Gatys, Alexander S. Ecker, and Matthias Bethge, Reproduced with permission. Lecture May 10, 2017
61 Neural Texture Synthesis Pretrain a CNN on ImageNet (VGG-19) Run input texture forward through CNN, record activations on every layer; layer i gives feature map of shape Ci Hi Wi At each layer compute the Gram matrix giving outer product of features: (shape Ci Ci) Initialize generated image from random noise Pass generated image through CNN, compute Gram matrix on each layer Compute loss: weighted sum of L2 distance between Gram matrices Backprop to get gradient on image Make gradient step on image GOTO 5 Gatys, Ecker, and Bethge, Texture Synthesis Using Convolutional Neural Networks, NIPS 2015 Figure copyright Leon Gatys, Alexander S. Ecker, and Matthias Bethge, Reproduced with permission. Lecture May 10, 2017
62 Neural Texture Synthesis Reconstructing texture from higher layers recovers larger features from the input texture Gatys, Ecker, and Bethge, Texture Synthesis Using Convolutional Neural Networks, NIPS 2015 Figure copyright Leon Gatys, Alexander S. Ecker, and Matthias Bethge, Reproduced with permission. Lecture May 10, 2017
63 Neural Texture Synthesis: Texture = Artwork Texture synthesis (Gram reconstruction) Figure from Johnson, Alahi, and Fei-Fei, Perceptual Losses for Real-Time Style Transfer and Super-Resolution, ECCV Copyright Springer, Reproduced for educational purposes. Lecture May 10, 2017
64 Neural Style Transfer: Feature + Gram Reconstruction Texture synthesis (Gram reconstruction) Feature reconstruction Figure from Johnson, Alahi, and Fei-Fei, Perceptual Losses for Real-Time Style Transfer and Super-Resolution, ECCV Copyright Springer, Reproduced for educational purposes. Lecture May 10, 2017
65 Neural Style Transfer Content Image Style Image + This image is licensed under CC-BY 3.0 Starry Night by Van Gogh is in the public domain Gatys, Ecker, and Bethge, Texture Synthesis Using Convolutional Neural Networks, NIPS 2015 Lecture May 10, 2017
66 Neural Style Transfer Content Image Style Image + This image is licensed under CC-BY 3.0 Style Transfer! = Starry Night by Van Gogh is in the public domain This image copyright Justin Johnson, Reproduced with permission. Gatys, Ecker, and Bethge, Image style transfer using convolutional neural networks, CVPR 2016 Lecture May 10, 2017
67 Style image Output image (Start with noise) Content image Gatys, Ecker, and Bethge, Image style transfer using convolutional neural networks, CVPR 2016 Figure adapted from Johnson, Alahi, and Fei-Fei, Perceptual Losses for Real-Time Style Transfer and Super-Resolution, ECCV Copyright Springer, Reproduced for educational purposes. Lecture May 10, 2017
68 Style image Output image Content image Gatys, Ecker, and Bethge, Image style transfer using convolutional neural networks, CVPR 2016 Figure adapted from Johnson, Alahi, and Fei-Fei, Perceptual Losses for Real-Time Style Transfer and Super-Resolution, ECCV Copyright Springer, Reproduced for educational purposes. Lecture May 10, 2017
69 Neural Style Transfer Example outputs from my implementation (in Torch) Gatys, Ecker, and Bethge, Image style transfer using convolutional neural networks, CVPR 2016 Figure copyright Justin Johnson, Lecture May 10, 2017
70 Neural Style Transfer More weight to content loss More weight to style loss Lecture May 10, 2017
71 Neural Style Transfer Resizing style image before running style transfer algorithm can transfer different types of features Larger style image Smaller style image Gatys, Ecker, and Bethge, Image style transfer using convolutional neural networks, CVPR 2016 Figure copyright Justin Johnson, Lecture May 10, 2017
72 Neural Style Transfer: Multiple Style Images Mix style from multiple images by taking a weighted average of Gram matrices Gatys, Ecker, and Bethge, Image style transfer using convolutional neural networks, CVPR 2016 Figure copyright Justin Johnson, Lecture May 10, 2017
73 Lecture May 10, 2017
74 Lecture May 10, 2017
75 Lecture May 10, 2017
76 Neural Style Transfer Problem: Style transfer requires many forward / backward passes through VGG; very slow! Lecture May 10, 2017
77 Neural Style Transfer Problem: Style transfer requires many forward / backward passes through VGG; very slow! Solution: Train another neural network to perform style transfer for us! Lecture May 10, 2017
78 Fast Style Transfer (1) (2) (3) Train a feedforward network for each style Use pretrained CNN to compute same losses as before After training, stylize images using a single forward pass 78 Johnson, Alahi, and Fei-Fei, Perceptual Losses for Real-Time Style Transfer and Super-Resolution, ECCV 2016 Figure copyright Springer, Reproduced for educational purposes. Lecture May 10, 2017
79 Fast Style Transfer Slow Fast Slow Fast Johnson, Alahi, and Fei-Fei, Perceptual Losses for Real-Time Style Transfer and Super-Resolution, ECCV 2016 Figure copyright Springer, Reproduced for educational purposes. Lecture May 10, 2017
80 Fast Style Transfer Concurrent work from Ulyanov et al, comparable results Ulyanov et al, Texture Networks: Feed-forward Synthesis of Textures and Stylized Images, ICML 2016 Ulyanov et al, Instance Normalization: The Missing Ingredient for Fast Stylization, arxiv 2016 Figures copyright Dmitry Ulyanov, Vadim Lebedev, Andrea Vedaldi, and Victor Lempitsky, Reproduced with permission. Lecture May 10, 2017
81 Fast Style Transfer Replacing batch normalization with Instance Normalization improves results Ulyanov et al, Texture Networks: Feed-forward Synthesis of Textures and Stylized Images, ICML 2016 Ulyanov et al, Instance Normalization: The Missing Ingredient for Fast Stylization, arxiv 2016 Figures copyright Dmitry Ulyanov, Vadim Lebedev, Andrea Vedaldi, and Victor Lempitsky, Reproduced with permission. Lecture May 10, 2017
82 One Network, Many Styles Dumoulin, Shlens, and Kudlur, A Learned Representation for Artistic Style, ICLR Figure copyright Vincent Dumoulin, Jonathon Shlens, and Manjunath Kudlur, 2016; reproduced with permission. Lecture May 10, 2017
83 One Network, Many Styles Use the same network for multiple styles using conditional instance normalization: learn separate scale and shift parameters per style Dumoulin, Shlens, and Kudlur, A Learned Representation for Artistic Style, ICLR Single network can blend styles after training Figure copyright Vincent Dumoulin, Jonathon Shlens, and Manjunath Kudlur, 2016; reproduced with permission. Lecture May 10, 2017
84 Summary Many methods for understanding CNN representations Activations: Nearest neighbors, Dimensionality reduction, maximal patches, occlusion Gradients: Saliency maps, class visualization, fooling images, feature inversion Fun: DeepDream, Style Transfer. Lecture May 10, 2017
85 Next time: Unsupervised Learning Autoencoders Variational Autoencoders Generative Adversarial Networks Lecture May 10, 2017
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