Basics for Convolutional Neural Network...Image Classification 28 x 28 = [28]2 CU DU CU [28] 2[14]...
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Basics for Convolutional Neural Network CSE 455 Beibin Li 2020-05-26
Transcript of Basics for Convolutional Neural Network...Image Classification 28 x 28 = [28]2 CU DU CU [28] 2[14]...
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Basics for
Convolutional Neural
NetworkCSE 455
Beibin Li
2020-05-26
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CNN
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Convolution OperationNowadays, we learn kernels from the data.
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Learning
● Details:
● https://www.slideshare.net/EdwinEfranJimnezLepe/example-feedforward-backpropagation
● https://medium.com/@2017csm1006/forward-and-backpropagation-in-convolutional-neural-network-4dfa96d7b37e
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Pooling
e.g. kernel size = 2, stride = 2 for both
width and height.
The kernel size for pooling can be an
even number.
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CNN StructuresImage Classification
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Image Classification
28 x 28 = [28]2
CU DU CU
[28]2 [14]2 [14]2
DU CU
[7]2 [7]2
GAP FC
[1]2
Cat
CU
DU GAP
FCConvolutional
Unit
Down-sampling Unit
Fully-connectedOr Linear Layer
Global Avg.Pooling
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Convolutional Unit (CU) - VGG
3x3 Conv Layer
3x3 Conv Layer
Image Source (Inception): Szegedy, Christian, et al. "Rethinking theinception architecture for computer vision." CVPR. 2016.
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Basic Block in ResNet
3x3 Conv Layer
3x3 Conv Layer
ResNet: He, Kaiming, et al. "Deep residual learningfor image recognition." CVPR. 2016.
• Residual Connection• Element-wise addition of input and output• Improves gradient flow and accuracy• In ResNet-18 and ResNet-34• Still computationally expensive
• Hard to train very deep networks (> 100 layers)
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Bottleneck in ResNet
3x3 Depth-Conv Layer
1x1 Conv Layer
• Used in ResNet-50, ResNet-101, ResNet-152, etc...• Computationally Efficient
Influence:• Bottleneck unit with Depth-wise convs
• MobileNetv2• ShuffleNetv2
• MobileNetv2: Sandler, Mark, et al. "Mobilenetv2: Inverted residuals andlinear bottlenecks." CVPR, 2018.
• ShuffleNetv2: Ma, Ningning, et al. "Shufflenet v2: Practical guidelines forefficient cnn architecture design." ECCV, 2018.
1x1 Conv Layer
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CNN StructuresSemantic Segmentation
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Encoder-Decoder
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Encoder-Decoder in Semantic Segmentation
CU DU CU DU CU
UUCUUUCU
CU DU GAPFCConvolutional
UnitDown-sampling
UnitFully-connectedOr Linear Layer
Global Avg.Pooling
UUUp-sampling
Unit
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U-Net
CU DU GAPFCConvolutional
UnitDown-sampling
UnitFully-connectedOr Linear Layer
Global Avg.Pooling
UUUp-sampling
Unit
CU DU CU DU CU
UUCUUUCU
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Deep Learning Libraries
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Homework 6
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Homework 6
Convolutional Neural Networks
In PyTorch(Optional Homework)
Due: June/4
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Neural Network (Q1)
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Convolutional Neural Network (Q2)
Conv
Pool
FC
Cross Entropy
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Yellow or Blue?
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Color Normalization (Q3)
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Deep Convolutional Neural Network (Q4)
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Make the Design More Flexible
Input:
[8, 16, 32, "pool"]
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Data Augmentation (Q5)
Random Affine Transformation
![Convolutional Codes R-J Chen. p2. OUTLINE [1] Shift registers and polynomials [2] Encoding convolutional codes [3] Decoding convolutional codes.](https://static.fdocuments.net/doc/165x107/5697c02a1a28abf838cd7c3c/convolutional-codes-r-j-chen-p2-outline-1-shift-registers-and-polynomials.jpg)
![Convolutional Codes. p2. OUTLINE [1] Shift registers and polynomials [2] Encoding convolutional codes [3] Decoding convolutional codes [4] Truncated.](https://static.fdocuments.net/doc/165x107/56649ec95503460f94bd6446/convolutional-codes-p2-outline-1-shift-registers-and-polynomials-.jpg)
