Convolutional Neural Networks Slides by Ian Goodfellow, Fei-Fei Li, Justin Johnson, Serena Yeung, Marc'aurelio Ranzato a Bit of History

CS6375: Machine Learning Gautam Kunapuli

Convolutional Neural Networks Slides by Ian Goodfellow, Fei-Fei Li, Justin Johnson, Serena Yeung, Marc'Aurelio Ranzato A bit of history...

[Hinton and Salakhutdinov 2006]

Reinvigorated research in Deep Learning

Illustration of Hinton and Salakhutdinov 2006 by Lane McIntosh, copyright CS231n 2017

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 5 - 8 April 18, 2017 First strong results

Acoustic Modeling using Deep Belief Networks Abdel-rahman Mohamed, George Dahl, Geoffrey Hinton, 2010 Context-Dependent Pre-trained Deep Neural Networks for Large Vocabulary Speech Recognition George Dahl, Dong Yu, Li Deng, Alex Acero, 2012

Imagenet classification with deep convolutional Illustration of Dahl et al. 2012 by Lane McIntosh, copyright neural networks CS231n 2017 Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton, 2012

Figures copyright Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, 2012. Reproduced with permission.

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 5 - 9 April 18, 2017 A bit of history:

Hubel & Wiesel, 1959 RECEPTIVE FIELDS OF SINGLE NEURONES IN THE CAT'S STRIATE CORTEX 1962 RECEPTIVE FIELDS, BINOCULAR INTERACTION AND FUNCTIONAL ARCHITECTURE IN THE CAT'S VISUAL CORTEX Cat image by CNX OpenStax is licensed 1968... under CC BY 4.0; changes made

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 5 - 1010 April 18, 2017 A bit of history: Gradient-based learning applied to document recognition [LeCun, Bottou, Bengio, Haffner 1998]

LeNet-5

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 5 - 1414 April 18, 2017 A bit of history: ImageNet Classification with Deep Convolutional Neural Networks [Krizhevsky, Sutskever, Hinton, 2012]

Figure copyright Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, 2012. Reproduced with permission. “AlexNet”

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 5 - 1515 April 18, 2017 Fast-forward to today: ConvNets are everywhere

Classification Retrieval

Figures copyright Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, 2012. Reproduced with permission.

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 5 - 16 April 18, 2017 Fast-forward to today: ConvNets are everywhere

Detection Segmentation

Figures copyright Shaoqing Ren, Kaiming He, Ross Girschick, Jian Sun, 2015. Reproduced with Figures copyright Clement Farabet, 2012. permission. Reproduced with permission. [Farabet et al., 2012] [Faster R-CNN: Ren, He, Girshick, Sun 2015] Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 5 - 17 April 18, 2017 Fast-forward to today: ConvNets are everywhere

This image by GBPublic_PR is licensed under CC-BY 2.0 NVIDIA Tesla line (these are the GPUs on rye01.stanford.edu)

Note that for embedded systems a typical setup

Photo by Lane McIntosh. Copyright CS231n 2017. would involve NVIDIA Tegras, with integrated self-driving cars GPU and ARM-based CPU cores.

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 5 - 18 April 18, 2017 Fast-forward to today: ConvNets are everywhere

[Taigman et al. 2014] Activations of inception-v3 architecture [Szegedy et al. 2015] to image of Emma McIntosh, used with permission. Figure and architecture not from Taigman et al. 2014.

Illustration by Lane McIntosh, Figures copyright Simonyan et al., 2014. photos of Katie Cumnock [Simonyan et al. 2014] Reproduced with permission. used with permission. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 5 - 19 April 18, 2017 Fast-forward to today: ConvNets are everywhere

Images are examples of pose estimation, not actually from Toshev & Szegedy 2014. Copyright Lane McIntosh. [Toshev, Szegedy 2014]

[Guo et al. 2014] Figures copyright Xiaoxiao Guo, Satinder Singh, Honglak Lee, Richard Lewis, and Xiaoshi Wang, 2014. Reproduced with permission. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 5 - 20 April 18, 2017 This image by Christin Khan is in the public domain Photo and figure by Lane McIntosh; not actual and originally came from the U.S. NOAA. example from Mnih and Hinton, 2010 paper.

Whale recognition, Kaggle Challenge Mnih and Hinton, 2010

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 5 - 22 April 18, 2017 No errors Minor errors Somewhat related Image Captioning

[Vinyals et al., 2015] [Karpathy and Fei-Fei, 2015]

A white teddy bear sitting in A man in a baseball A woman is holding a the grass uniform throwing a ball cat in her hand

All images are CC0 Public domain: https://pixabay.com/en/luggage-antique-cat-1643010/ https://pixabay.com/en/teddy-plush-bears-cute-teddy-bear-1623436/ https://pixabay.com/en/surf-wave-summer-sport-litoral-1668716/ https://pixabay.com/en/woman-female-model-portrait-adult-983967/ https://pixabay.com/en/handstand-lake-meditation-496008/ A man riding a wave on A cat sitting on a A woman standing on a https://pixabay.com/en/baseball-player-shortstop-infield-1045263/

top of a surfboard suitcase on the floor beach holding a surfboard Captions generated by Justin Johnson using Neuraltalk2

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 5 - 23 April 18, 2017 Convolutional Networks

• Scale up neural networks to process very large images / video sequences

• Sparse connections

• Parameter sharing

• Automatically generalize across spatial translations of inputs

• Applicable to any input that is laid out on a grid (1-D, 2-D, 3-D, …)

(Goodfellow 2016) CHAPTER 9. CONVOLUTIONAL NETWORKS

Sparse Connectivity

Sparse s1 s2 s3 s4 s5 connections due to small convolution x x x x x kernel 1 2 3 4 5

s1 s2 s3 s4 s5 Dense connections

x1 x2 x3 x4 x5

Figure 9.2 (Goodfellow 2016) Figure 9.2: Sparse connectivity, viewed from below: We highlight one input unit, x3, and also highlight the output units in s that are affected by this unit. (Top)When s is formed by convolution with a kernel of width 3,onlythreeoutputsareaffected by x. (Bottom)When s is formed by matrix multiplication, connectivity is no longer sparse, so all of the outputs are affected by x3.

336 CHAPTERSparse 9. CONVOLUTIONAL Connectivity NETWORKS

Sparse s1 s2 s3 s4 s5 connections due to small convolution x1 x2 x3 x4 x5 kernel

s1 s2 s3 s4 s5 Dense connections

x1 x2 x3 x4 x5

(Goodfellow 2016) Figure 9.3: Sparse connectivity, viewedFigure from 9.3 above: We highlight one output unit, s3, and also highlight the input units in x that affect this unit. These units are known as the receptive field of s3. (Top)When s is formed by convolution with a kernel of width 3,onlythreeinputsaffect s3. (Bottom)When s is formed by matrix multiplication, connectivity is no longer sparse, so all of the inputs affect s3.

g1 g2 g3 g4 g5

h1 h2 h3 h4 h5

x1 x2 x3 x4 x5

Figure 9.4: The receptive field of the units in the deeper layers of a convolutional network is larger than the receptive field of the units in the shallow layers. This effect increases if the network includes architectural features like strided convolution (figure 9.12) or pooling (section 9.3). This means that even though direct connections in a convolutional net are very sparse, units in the deeper layers can be indirectly connected to all or most of the input image.

337 CHAPTER 9. CONVOLUTIONAL NETWORKS

s1 s2 s3 s4 s5

x1 x2 x3 x4 x5

s1 s2 s3 s4 s5

x1 x2 x3 x4 x5

Figure 9.3: Sparse connectivity, viewed from above: We highlight one output unit, s3, and also highlight the input units in x that affect this unit. These units are known as the receptive field of s3. (Top)When s is formed by convolution with a kernel of width 3,onlythreeinputsaffect s3. (Bottom)When s is formed by matrix multiplication, connectivity is noGrowing longer sparse, so allReceptive of the inputs affect s3Fields.

g1 g2 g3 g4 g5

h1 h2 h3 h4 h5

x1 x2 x3 x4 x5

Figure 9.4: The receptive field of the units in the deeper layers of a convolutional network is larger than the receptive field of the units in the shallow layers. This effect increases if the network includes architectural featuresFigure like strided9.4 convolution (figure(Goodfellow 9.12) 2016) or pooling (section 9.3). This means that even though direct connections in a convolutional net are very sparse, units in the deeper layers can be indirectly connected to all or most of the input image.

337 CHAPTERParameter 9. CONVOLUTIONAL NETWORKS Sharing Convolution s1 s2 s3 s4 s5 shares the same parameters across all spatial x1 x2 x3 x4 x5 locations Traditional s1 s2 s3 s4 s5 matrix multiplication does not share x1 x2 x3 x4 x5 any parameters Figure 9.5: Parameter sharing: Black arrows indicate the connections that use a particular (Goodfellow 2016) parameter in two diﬀerent models.Figure (Top)9.5 The black arrows indicate uses of the central element of a 3-element kernel in a convolutional model. Due to parameter sharing, this single parameter is used at all input locations. (Bottom)The single black arrow indicates the use of the central element of the weight matrix in a fully connected model. This model has no parameter sharing so the parameter is used only once.

for every location, we learn only one set. This does not affect the runtime of forward propagation—it is still O(k n)—but it does further reduce the storage ⇥ requirements of the model to k parameters. Recall that k is usually several orders of magnitude less than m. Since m and n are usually roughly the same size, k is practically insignificant compared to m n. Convolution is thus dramatically more ⇥ efficient than dense matrix multiplication in terms of the memory requirements and statistical efficiency. For a graphical depiction of how parameter sharing works, see figure 9.5. As an example of both of these first two principles in action, figure 9.6 shows how sparse connectivity and parameter sharing can dramatically improve the efficiency of a linear function for detecting edges in an image. In the case of convolution, the particular form of parameter sharing causes the layer to have a property called equivariance to translation. To say a function is equivariant means that if the input changes, the output changes in the same way. Specifically, a function f(x) is equivariant to a function g if f(g(x)) = g(f(x)). In the case of convolution, if we let g be any function that translates the input, i.e., shifts it, then the convolution function is equivariant to g. For example, let I be a function giving image brightness at integer coordinates. Let g be a function

338 CHAPTER 9. CONVOLUTIONAL NETWORKS

CHAPTER 9. CONVOLUTIONAL NETWORKS Edge Detection by Convolution

Input Figure 9.6: Efficiency of edge detection. The image on the right was formed by taking each pixel in the original image and subtracting the value of its neighboring pixel on the left. This shows the strength of all of the vertically oriented edges in the input image, which can be a useful operation for object detection. Both images are 280 pixels tall. -1 -1 The input image isFigure 320 pixels 9.6: wideEfficiency while of the edge output detection image. The is 319 image pixels onOutput the wide. right This was formed by taking transformation can beeach described pixel in theby a original convolution image kernel and subtracting containing the two value elements, of its neighboring and pixel on the requires 319 280 3 =Kernel 267, 960 floating point operations (two multiplications and ⇥ ⇥left. This shows the strength of all of the vertically oriented edges in the input image, one addition per outputwhich pixel) can be to a compute useful operation using convolution. for object detection. To describe Both the images same are 280 pixels tall. transformation with a matrix multiplication would take 320 280 319 280,orover The input image is 320 pixels wide while⇥ the⇥ output⇥ image is 319 pixels(Goodfellow wide. 2016) This eight billion, entries intransformation the matrix, making can be convolution describedFigure by four 9.6 a billionconvolution times kernel more e containingfficient for two elements, and representing this transformation.requires 319 The280 straightforward3 = 267, 960 matrixfloating multiplication point operations algorithm (two multiplications and ⇥ ⇥ performs over sixteenone billion addition floating per point output operations, pixel) to making compute convolution using convolution. roughly 60,000 To describe the same times more efficient computationally.transformation with Of course, a matrix most multiplication of the entries would of the takematrix320 would280 be 319 280,orover ⇥ ⇥ ⇥ zero. If we stored onlyeight the billion, nonzero entries entries in of the the matrix, matrix, making then both convolution matrix four multiplication billion times more efficient for and convolution wouldrepresenting require the this same transformation. number of floating The straightforward point operations matrix to compute. multiplication algorithm The matrix would still need to contain 2 319 280 = 178, 640 entries. Convolution performs over sixteen billion⇥ floating⇥ point operations, making convolution roughly 60,000 is an extremely efficienttimes way more of effi describingcient computationally. transformations Of course, that apply most theof the same entries linear of the matrix would be transformation of azero. small, If local we stored region only across the nonzero the entire entries input. of the (Photo matrix, credit: then both Paula matrix multiplication Goodfellow) and convolution would require the same number of floating point operations to compute. The matrix would still need to contain 2 319 280 = 178, 640 entries. Convolution ⇥ ⇥ is an extremely efficient way of describing transformations that apply the same linear transformation of a small, local region across the entire input. (Photo credit: Paula Goodfellow)

340

340 Eﬃciency of Convolution Input size: 320 by 280 Kernel size: 2 by 1 Output size: 319 by 280

Convolution Dense matrix Sparse matrix

319*280*320*280 2*319*280 = Stored ﬂoats 2 > 8e9 178,640

Same as Float muls or 319*280*3 = > 16e9 convolution adds 267,960 (267,960)

(Goodfellow 2016) CHAPTER 9. CONVOLUTIONAL NETWORKS Convolutional Network Components

Complex layer terminology Simple layer terminology

Next layer Next layer

Convolutional Layer

Pooling stage Pooling layer

Detector stage: Detector layer: Nonlinearity Nonlinearity e.g., rectiﬁed linear e.g., rectiﬁed linear

Convolution stage: Convolution layer: Aﬃne transform Aﬃne transform

Input to layer Input to layers

Figure 9.7: The components of a typical convolutional neural network layer. There are two commonly used sets of terminologyFigure for describing 9.7 these layers. (Left)In this terminology, (Goodfellow 2016) the convolutional net is viewed as a small number of relatively complex layers, with each layer having many “stages.” In this terminology, there is a one-to-one mapping between kernel tensors and network layers. In this book we generally use this terminology. (Right)In this terminology, the convolutional net is viewed as a larger number of simple layers; every step of processing is regarded as a layer in its own right. This means that not every “layer” has parameters.

341 Fully Connected Layer

Example: 200x200 image 40K hidden units ~2B parameters!!!

- Spatial correlation is local - Waste of resources + we have not enough 33 training samples anyway.. Ranzato Locally Connected Layer

STATIONARITY? Statistics is similar at different locations

Example: 200x200 image 40K hidden units Filter size: 10x10 4M parameters

Note: This parameterization is good when input image is registered (e.g., 35 face recognition). Ranzato Convolutional Layer

Learn multiple filters.

E.g.: 200x200 image 100 Filters Filter size: 10x10 10K parameters

Ranzato Fully Connected Layer

32x32x3 image -> stretch to 3072 x 1

input activation

1 1 10 x 3072 3072 10 weights 1 number: the result of taking a dot product between a row of W and the input (a 3072-dimensional dot product)

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 5 - 27 April 18, 2017 Convolution Layer Filters always extend the full depth of the input volume 32x32x3 image

5x5x3 filter

Convolve the filter with the image i.e. “slide over the image spatially, computing dot products”

32 3

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 5 - 30 April 18, 2017 Convolution Layer 32x32x3 image 5x5x3 filter 32

1 number: the result of taking a dot product between the filter and a small 5x5x3 chunk of the image 32 (i.e. 5*5*3 = 75-dimensional dot product + bias) 3

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 5 - 31 April 18, 2017 Convolution Layer activation map 32x32x3 image 5x5x3 filter 32

convolve (slide) over all spatial locations

32 28 3 1

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 5 - 32 April 18, 2017 Convolution Layer consider a second, green filter 32x32x3 image activation maps 5x5x3 filter 32

convolve (slide) over all spatial locations

32 28 3 1

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 5 - 33 April 18, 2017 For example, if we had 6 5x5 filters, we’ll get 6 separate activation maps: activation maps

Convolution Layer

32 28 3 6 We stack these up to get a “new image” of size 28x28x6!

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 5 - 34 April 18, 2017 Preview: ConvNet is a sequence of Convolution Layers, interspersed with activation functions

32 28

CONV, ReLU e.g. 6 5x5x3 32 filters 28 3 6

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 5 - 35 April 18, 2017 Preview: ConvNet is a sequence of Convolutional Layers, interspersed with activation functions

32 28 24

…. CONV, CONV, CONV, ReLU ReLU ReLU e.g. 6 e.g. 10 5x5x3 5x5x6 32 filters 28 filters 24 3 6 10

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 5 - 36 April 18, 2017 CHAPTER 9. CONVOLUTIONAL NETWORKS Convolution with Stride

s1 s2 s3

Strided convolution

x1 x2 x3 x4 x5

s1 s2 s3

Downsampling

z1 z2 z3 z4 z5

Convolution Figure 9.12

x1 x2 x3 x4 x5

(Goodfellow 2016) Figure 9.12: Convolution with a stride. In this example, we use a stride of two. (Top)Convolution with a stride length of two implemented in a single operation. (Bot- tom)Convolution with a stride greater than one pixel is mathematically equivalent to convolution with unit stride followed by downsampling. Obviously, the two-step approach involving downsampling is computationally wasteful, because it computes many values that are then discarded.

350 A closer look at spatial dimensions: