[1]Deep Learning: a theoretical introduction – Episode 3

Deep Learningand TensorFlowEpisode 3 Deep Convolutional Neural Networks

Università degli Studi di Pavia

[2]Deep Learning: a theoretical introduction – Episode 3

The storm ofDeep Convolutional Neural Networks

(DCNN)

[3]Deep Learning: a theoretical introduction – Episode 3

ImageNet Challenge▪

[4]Deep Learning: a theoretical introduction – Episode 3

ImageNet Challenge▪

[5]Deep Learning: a theoretical introduction – Episode 3

▪

The Mother of all DCNNs

[6]Deep Learning: a theoretical introduction – Episode 3

▪

The Mother of all DCNNs

[7]Deep Learning: a theoretical introduction – Episode 3

DCNN Building Blocks(layerwise)

[8]Deep Learning: a theoretical introduction – Episode 3

Convolutional Layer▪

[9]Deep Learning: a theoretical introduction – Episode 3

Convolutional Layer▪

i

[10]Deep Learning: a theoretical introduction – Episode 3

Convolutional Layer▪

[11]Deep Learning: a theoretical introduction – Episode 3

Convolutional Layer▪

[12]Deep Learning: a theoretical introduction – Episode 3

Max Pooling Layer▪

[13]Deep Learning: a theoretical introduction – Episode 3

Local Response Normalization Layer▪

•

•

[14]Deep Learning: a theoretical introduction – Episode 3

▪

•

•

•

•

AlexNet Architecture

[15]Deep Learning: a theoretical introduction – Episode 3

AlexNet Gradient▪

Loss Function

[16]Deep Learning: a theoretical introduction – Episode 3

Convolutional Layer Gradient▪

m

0,0

[17]Deep Learning: a theoretical introduction – Episode 3

Convolutional Layer Gradient▪

E

*W X

Y

[18]Deep Learning: a theoretical introduction – Episode 3

Convolutional Layer Gradient▪

E

*W X

Y

[19]Deep Learning: a theoretical introduction – Episode 3

Convolutional Layer Gradient▪

E

*W X

Y

[20]Deep Learning: a theoretical introduction – Episode 3

Convolutional Layer Gradient▪

E

*W X

Y

Yij

[21]Deep Learning: a theoretical introduction – Episode 3

Convolutional Layer Gradient▪

E

*W X

Y

X

[22]Deep Learning: a theoretical introduction – Episode 3

Convolutional Layer Gradient▪

E

*W X

Y

X

X

[23]Deep Learning: a theoretical introduction – Episode 3

Convolutional Layer Gradient▪

E

*W X

Y

X

[24]Deep Learning: a theoretical introduction – Episode 3

Convolutional Layer Gradient▪

E

ReLU

X

Y

[25]Deep Learning: a theoretical introduction – Episode 3

Convolutional Layer Gradient▪

E

ReLU

X

Y

Yij = 1 = 0

[26]Deep Learning: a theoretical introduction – Episode 3

Max Pooling Gradient▪

[27]Deep Learning: a theoretical introduction – Episode 3

Convolutional Layer Gradient▪

[28]Deep Learning: a theoretical introduction – Episode 3

Convolutional Layer Gradient▪

[29]Deep Learning: a theoretical introduction – Episode 3

LRN Gradient▪

[30]Deep Learning: a theoretical introduction – Episode 3

LRN Gradient▪

[31]Deep Learning: a theoretical introduction – Episode 3

LRN Gradient▪

E

LRN

X

Y

Yijl

[32]Deep Learning: a theoretical introduction – Episode 3

LRN Gradient▪

E

LRN

X

Y

X

[33]Deep Learning: a theoretical introduction – Episode 3

ImageNet Challenge▪

[34]Deep Learning: a theoretical introduction – Episode 3

AlexNet (Krizhevsky, Sutskever & Hinton, 2012)

•

•

[35]Deep Learning: a theoretical introduction – Episode 3

Deep Convolutional Neural Networks (DCNN)▪

[36]Deep Learning: a theoretical introduction – Episode 3

Inside AlexNet(after training)

[37]Deep Learning: a theoretical introduction – Episode 3

AlexNet Filters (after training)

[38]Deep Learning: a theoretical introduction – Episode 3

AlexNet Filters- DeconvNet

[39]Deep Learning: a theoretical introduction – Episode 3

AlexNet Filters- DeconvNet

[40]Deep Learning: a theoretical introduction – Episode 3

AlexNet Filters- DeconvNet

[41]Deep Learning: a theoretical introduction – Episode 3

AlexNet Filters- DeconvNet

[42]Deep Learning: a theoretical introduction – Episode 3

Beyond AlexNet:The DCNN storm

[43]Deep Learning: a theoretical introduction – Episode 3

ImageNet: the full story

[44]Deep Learning: a theoretical introduction – Episode 3

VGG Architecture

[45]Deep Learning: a theoretical introduction – Episode 3

Inception Acrhitecture▪

[46]Deep Learning: a theoretical introduction – Episode 3

Inception Architecture▪

[47]Deep Learning: a theoretical introduction – Episode 3

Inception Architecture▪

h

d h d

[48]Deep Learning: a theoretical introduction – Episode 3

Inception Architecture▪

[49]Deep Learning: a theoretical introduction – Episode 3

Inception Architecture▪

256 480 480512

512 512 832

832 1024

[50]Deep Learning: a theoretical introduction – Episode 3

Inception Architecture▪

[51]Deep Learning: a theoretical introduction – Episode 3

Inception Architecture▪

[52]Deep Learning: a theoretical introduction – Episode 3

ResNet Architecture▪

[53]Deep Learning: a theoretical introduction – Episode 3

ResNet Architecture▪

[54]Deep Learning: a theoretical introduction – Episode 3

Comparing Different DCNNs▪

[55]Deep Learning: a theoretical introduction – Episode 3

Comparing Different DCNNs

[56]Deep Learning: a theoretical introduction – Episode 3

Do DCNNs Dreamof Electric Sheep?

[57]Deep Learning: a theoretical introduction – Episode 3

Can DCNNs 'dream'?

[58]Deep Learning: a theoretical introduction – Episode 3

Can DCNNs 'dream'?

[59]Deep Learning: a theoretical introduction – Episode 3

Feature Enhancement▪

k l I

[60]Deep Learning: a theoretical introduction – Episode 3

Can DCNNs 'dream'?

[61]Deep Learning: a theoretical introduction – Episode 3

Can DCNNs 'dream'?

[62]Deep Learning: a theoretical introduction – Episode 3

Can DCNNs 'dream'?

[63]Deep Learning: a theoretical introduction – Episode 3

Can DCNNs 'dream'?

[64]Deep Learning: a theoretical introduction – Episode 3

The Power of Abstraction(in layers)

[65]Deep Learning: a theoretical introduction – Episode 3

▪

The Power of Abstraction

[66]Deep Learning: a theoretical introduction – Episode 3

▪

The Power of Abstraction

[67]Deep Learning: a theoretical introduction – Episode 3

Mixing Two Images▪

k l I

[68]Deep Learning: a theoretical introduction – Episode 3

▪

The Power of Abstraction

[69]Deep Learning: a theoretical introduction – Episode 3

▪

The Power of Abstraction

[70]Deep Learning: a theoretical introduction – Episode 3

Human-like Vision?

[71]Deep Learning: a theoretical introduction – Episode 3

A DCNN can be fooled…

[72]Deep Learning: a theoretical introduction – Episode 3

Reconstructing Images from Feature Maps

[73]Deep Learning: a theoretical introduction – Episode 3

Reconstructing Images from Feature Maps▪

k l I

[74]Deep Learning: a theoretical introduction – Episode 3

Reconstructing Images from Feature Maps

[75]Deep Learning: a theoretical introduction – Episode 3

Just add some little noise ...

[76]Deep Learning: a theoretical introduction – Episode 3

No Free Lunch:having an annotated dataset

[77]Deep Learning: a theoretical introduction – Episode 3

Generative Adversarial Network▪

[78]Deep Learning: a theoretical introduction – Episode 3

Active Learning

[79]Deep Learning: a theoretical introduction – Episode 3

Transfer Learning

[80]Deep Learning: a theoretical introduction – Episode 3

Transfer Learning

[81]Deep Learning: a theoretical introduction – Episode 3

Image ClassificationObject Detection

Segmentation

[82]Deep Learning: a theoretical introduction – Episode 3

Deep Learning for different imaging tasks

[83]Deep Learning: a theoretical introduction – Episode 3

Semantic segmentation

▪

[84]Deep Learning: a theoretical introduction – Episode 3

Object detection and positioning

▪

[85]Deep Learning: a theoretical introduction – Episode 3

Object detection and positioning

▪

[86]Deep Learning: a theoretical introduction – Episode 3

Object detection and positioning▪