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14-05-resnets.html
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<!DOCTYPE html>
<html>
<head>
<title>14.5 - ResNets [Andrei Bursuc]</title>
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<textarea id="source">
layout: true
.center.footer[Marc LELARGE and Andrei BURSUC | Deep Learning Do It Yourself | 14.5 ResNets]
---
class: center, middle, title-slide
count: false
## Going Deeper
# 14.5 ResNets
<br/>
<br/>
.bold[Andrei Bursuc ]
<br/>
url: https://dataflowr.github.io/website/
.citation[
With slides from A. Karpathy, F. Fleuret, G. Louppe, C. Ollion, O. Grisel, Y. Avrithis ...]
---
class: middle, center
## How deep can we go now?
---
# A saturation point
If we continue stacking more layers on a CNN:
.center.width-90[]
--
count: false
.center[.red[Deeper models are harder to optimize]]
---
.left-column[
# ResNet
]
.right-column[
.center.width-50[]
]
A block learns the residual w.r.t. identity
.center.width-40[]
.citation[K. He et al., Deep residual learning for image recognition, CVPR 2016.]
--
count: false
- Good optimization properties
---
.left-column[
# ResNet
]
.right-column[
.center.width-50[]
]
Even deeper models:
34, 50, 101, 152 layers
.citation[K. He et al., Deep residual learning for image recognition, CVPR 2016.]
---
.left-column[
# ResNet
]
.right-column[
.center.width-50[]
]
ResNet50 Compared to VGG:
- Superior accuracy in all vision tasks <br/>**5.25%** top-5 error vs 7.1%
.citation[K. He et al., Deep residual learning for image recognition, CVPR 2016.]
--
count: false
- Less parameters <br/>**25M** vs 138M
--
count: false
- Computational complexity <br/>**3.8B Flops** vs 15.3B Flops
--
count: false
- Fully Convolutional until the last layer
---
# ResNet
## Performance on ImageNet
.center.width-90[]
---
# ResNet
## Results
.center.width-100[]
---
# ResNet
## Results
.center.width-60[]
---
# ResNet
In PyTorch:
```py
def make_resnet_block(num_feature_maps , kernel_size = 3):
return nn.Sequential(
nn.Conv2d(num_feature_maps , num_feature_maps ,
kernel_size = kernel_size ,
padding = (kernel_size - 1) // 2),
nn.BatchNorm2d(num_feature_maps),
nn.ReLU(inplace = True),
nn.Conv2d(num_feature_maps , num_feature_maps ,
kernel_size = kernel_size ,
padding = (kernel_size - 1) // 2),
nn.BatchNorm2d(num_feature_maps),
)
```
---
# ResNet
In PyTorch:
```py
def __init__(self, num_residual_blocks, num_feature_maps)
...
self.resnet_blocks = nn.ModuleList()
for k in range(nb_residual_blocks):
self.resnet_blocks.append(make_resnet_block(num_feature_maps , 3))
...
```
```py
def forward(self,x):
...
for b in self.resnet_blocks:
* x = x + b(x)
...
return x
```
---
For ResNet50+ layers some additional modifications need to be made to keep number of parameters and computations manageable
.center.width-70[]
Such a block requires $2 \times (3 \times 3 \times 256 +1) \times 256 \simeq 1.2M$ parameters
.credit[Credits: F. Fleuret, [EE-559 Deep Learning](https://fleuret.org/dlc/), EPFL]
--
count: false
Adress this problem using __bottleneck__ block
.center.width-100[]
.center[$256 \times 64 + (3 \times 3 \times 64 +1) \times 64 + 64 \times 256 \simeq 70K$ parameters]
---
# Stochastic Depth Networks
- DropOut at layer level
- Allows training up to 1K layers
.center.width-70[]
.citation[Huang et al., Deep Networks with Stochastic Depth, ECCV 2016]
---
# DenseNet
- Copying feature maps to upper layers via skip-connections
- Better reuse of parameters and redundancy avoidance
.center.width-30[]
.center.width-70[]
.citation[Huang et al., Densely Connected Convolutional Networks, CVPR 2017]
---
# Visualizing loss surfaces
.center.width-70[]
.citation[H. Li et al., Visualizing the Loss Landscape of Neural Nets, ICLR workshop 2018]
---
# Visualizing loss surfaces
.left-column[
.center.width-100[]
]
.right-column[
<br><br><br><br><br>
- ResNet-20/56/110 : vanilla
- ResNet-*-noshort: no skip connections
- ResNet-18/34/50 : wide
]
.citation[H. Li et al., Visualizing the Loss Landscape of Neural Nets, ICLR workshop 2018]
---
# Visualizing loss surfaces
.center.width-50[]
.citation[H. Li et al., Visualizing the Loss Landscape of Neural Nets, ICLR workshop 2018]
---
# Outline
## Universal approximation theorem
## Why going deeper?
## Regularization in deep networks
### classic regularization: $L\_2$ regularization
### implicit regularization: Dropout, Batch Normalization
## Residual networks
---
class: end-slide, center
count: false
The end.
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