This document summarizes recent developments in convolutional neural networks (CNNs) for image recognition, including residual networks (ResNets) and densely connected convolutional networks (DenseNets). It reviews CNN structure and components like convolution, pooling, and ReLU. ResNets address degradation problems in deep networks by introducing identity-based skip connections. DenseNets connect each layer to every other layer to encourage feature reuse, addressing vanishing gradients. The document outlines the structures of ResNets and DenseNets and their advantages over traditional CNNs.

1. Comprehension
of deep-learning
- CNN from VGG to DenseNet
19.07.18 You Sung Min

2. 1. Review of Deep learning
(Convolutional Neural Network)
2. Residual network (Resnet)
He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings
of the IEEE conference on computer vision and pattern recognition. 2016.
3. Densely connected convolutional network
(DenseNet)
Huang, Gao, et al. "Densely connected convolutional networks." Proceedings of
the IEEE conference on computer vision and pattern recognition. 2017.
Contents

3. Structure of Neural Networks
 A simple model to emulate a single neuron
 This model produces a binary output
Review of Deep learning
=
𝟎 𝒊𝒇
𝒋
𝝎𝒋 𝒙𝒋 ≤ 𝑻
𝟏 𝒊𝒇
𝒋
𝝎𝒋 𝒙𝒋 > 𝑻
𝝎 𝟏
𝝎 𝟐
𝝎 𝟑
𝒋
𝝎𝒋 𝒙𝒋Inputs
Threshold T
Perceptron (1950) Neuron

4. Review of Deep learning
Multilayer Perceptron (MLP)
 A network model consists of perceptrons
 This model produces vectorized outputs

5. Multilayer Perceptron (MLP)
Review of Deep learning
Handwritten digit with
28 by 28 pixel image
Binary Input
(Intensity of a pixel)
28
28
Input
(784)
Desired output for “5”
𝒚(𝒙) = 𝟎, 𝟎, 𝟎, 𝟎, 𝟏, 𝟎, 𝟎, 𝟎, 𝟎 𝑻

6. Convolutional Neural Network
 Convolution layer
 Subsampling (Pooling) layer
 Rectified Linear Unit(ReLU)
Review of Deep learning
Feature Extractor Classifier

7. Review of Deep learning
 Local receptive field (connectivity)
28 by 28 23 by 23
5 by 5
Kernel
(window)
2D Convolution
1. Detect local information
(features)
(e.g., Edge, Shape)
2. Reduce connections
between layers
• Fully connected network
→ 𝟐𝟖 ∗ 𝟐𝟖 ∗ 𝟐𝟑 ∗ 𝟐3 connections
• Local connected network
→ 𝟓 ∗ 𝟓 ∗ 𝟐𝟑 ∗ 𝟐𝟑 connections
𝑤11 𝑤12
𝑤55

8. Review of Deep learning
 Shared weights
1. Detect same feature
in other positions
2. Reduce total number of
weights and bias
3. Construct multiple feature
maps (kernels)
𝒐𝒖𝒕𝒑𝒖𝒕 = 𝝈(𝒃 +
𝒍=𝟎
𝟒
𝒎=𝟎
𝟒
𝝎𝒍,𝒎 𝒂𝒋+𝒍,𝒌+𝒎)

9. Review of Deep learning
 Pooling layer
1. Simplify (condense)
information in the feature
map
2. Reduce connections
(weights and biases)
Max-pooling:
Output only maximum activation
Conv. Pooling

10. Convolutional Neural Network
Review of Deep learning

11. Convolutional Neural Network
Review of Deep learning
y = max(x,0)

12. Convolutional Neural Network
Review of Deep learning
Feature map

13.  Deeper neural networks are more difficult to train
 Vanishing (or exploding) gradient problem
 Degradation problem
Residual network (ResNet)

14.  Residual learning
 Desired underlying mapping: 𝓗(𝒙)
 Nonlinear stacked layer mapping: ℱ 𝒙 ≔ ℋ 𝒙 − 𝒙
 ∴ ℋ 𝒙 = ℱ 𝒙 + 𝒙
Residual network (ResNet)
Residual mapping

15. Residual network (ResNet)

16. Residual network (ResNet)

17. Residual network (ResNet)
Degradation problem

18. Residual network (ResNet)
 A: zero-padding shortcuts
 B: project shortcut for increasing
dimension, other shortcut are
identity
 C: all shortcuts are projections

19.  Dense block
 Short paths from early layers to later layers
Densely connected convolutional network
 Connect all layer (with
matching feature-map
size) directly
 Combine feature by
concatenating
 ∴ 𝑳-layer has
𝑳 𝑳+𝟏
𝟐
conntections

20.  DenseNet
 ResNets output 𝒙𝒍 = 𝑯𝒍 𝒙𝒍−𝟏 + 𝒙𝒍−𝟏
 DenseNets output 𝒙𝒍 = 𝑯𝒍 𝒙 𝟎, 𝒙 𝟏, … , 𝒙𝒍−𝟏
𝑤ℎ𝑒𝑟𝑒, 𝑯𝒍 𝒊𝒔 𝑩𝑵 + 𝑹𝒆𝑳𝑼 + 𝟑 ∗ 𝟑 𝒄𝒐𝒏𝒗𝒐𝒍𝒖𝒕𝒊𝒐𝒏
Densely connected convolutional network
AVP: average pooling
Transition layer
1 by 1 Conv
2 by 2 AVP

21.  Collective knowledge: very narrow layers (e.g., 12)
 Bottleneck layer: 1 by 1 conv before 3 by 3 conv
 Compression: reduce # of feature-map at transition layer
Densely connected convolutional network

22. Densely connected convolutional network

23. Densely connected convolutional network

24. References
 Image Source from https://deeplearning4j.org/convolutionalnets
 Zeiler, Matthew D., and Rob Fergus. "Visualizing and understanding
convolutional networks.“ European Conference on Computer Vision,
Springer International Publishing, 2014.
 Jia-Bin Huang, “Lecture 29 Convolutional Neural Networks”,
Computer Vision Spring 2015
 He, Kaiming, et al. "Deep residual learning for image
recognition." Proceedings of the IEEE conference on computer vision
and pattern recognition. 2016.
 Huang, Gao, et al. "Densely connected convolutional
networks." Proceedings of the IEEE conference on computer vision
and pattern recognition. 2017.