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Questions and Answers
What is a major issue with adding more fully connected (FC) layers to a neural network?
What is a major issue with adding more fully connected (FC) layers to a neural network?
- Network becomes arbitrarily complex (correct)
- Weight sharing becomes difficult
- Optimization becomes easier
- Performance never drops
To counter the problem of memorizing data when adding more layers to a network, what do we want instead?
To counter the problem of memorizing data when adding more layers to a network, what do we want instead?
- Layers with structure (correct)
- Decreasing weight sharing
- Increasing network complexity
- No structure in layers
Which aspect makes it challenging to manage weights and matrix multiplications when adding more FC layers?
Which aspect makes it challenging to manage weights and matrix multiplications when adding more FC layers?
- Adding complexity
- Decreasing the number of layers
- Using the same weights for different parts of the image (correct)
- Transitioning to simpler models
What feature of Convolutional Neural Networks (CNNs) distinguishes them from fully connected neural networks?
What feature of Convolutional Neural Networks (CNNs) distinguishes them from fully connected neural networks?
What is one consequence of going further deeper in network complexity without proper structure in the layers?
What is one consequence of going further deeper in network complexity without proper structure in the layers?
Why do Convolutional Neural Networks (CNNs) use weight sharing?
Why do Convolutional Neural Networks (CNNs) use weight sharing?
What is the main purpose of the LeNet-5 architecture described in the text?
What is the main purpose of the LeNet-5 architecture described in the text?
In the LeNet-5 architecture, what layer typically follows the Convolution-ReLU-Pool sequence?
In the LeNet-5 architecture, what layer typically follows the Convolution-ReLU-Pool sequence?
What kind of propagation is involved in training a CNN with Keras as mentioned in the text?
What kind of propagation is involved in training a CNN with Keras as mentioned in the text?
What is the purpose of the 'Linear(120→80)' layer in the LeNet-5 architecture?
What is the purpose of the 'Linear(120→80)' layer in the LeNet-5 architecture?
Which layer in the LeNet-5 architecture would be responsible for converting a 5x5x16 output to 120 units?
Which layer in the LeNet-5 architecture would be responsible for converting a 5x5x16 output to 120 units?
When was the LeNet-5 architecture created according to the text?
When was the LeNet-5 architecture created according to the text?
What was the purpose of LeNet-5 architecture created by Yann LeCun in 1998?
What was the purpose of LeNet-5 architecture created by Yann LeCun in 1998?
What is the function of the ReLU layer in the LeNet-5 architecture?
What is the function of the ReLU layer in the LeNet-5 architecture?
In the LeNet-5 architecture, what does the 'Convolution-2 (f=5, k=6) 62828' layer indicate?
In the LeNet-5 architecture, what does the 'Convolution-2 (f=5, k=6) 62828' layer indicate?
What is the purpose of the 'MaxPool-2 (f=2, s=2) 61414' layer in the LeNet-5 architecture?
What is the purpose of the 'MaxPool-2 (f=2, s=2) 61414' layer in the LeNet-5 architecture?
What happens in the 'Linear(12080) 80' layer of the LeNet-5 architecture?
What happens in the 'Linear(12080) 80' layer of the LeNet-5 architecture?
Which layer comes immediately after the 'MaxPool (f=2, s=2) 61414' in the LeNet-5 architecture?
Which layer comes immediately after the 'MaxPool (f=2, s=2) 61414' in the LeNet-5 architecture?
Who authored the groundbreaking research paper 'Cognitron: A self-organizing multilayered neural network'?
Who authored the groundbreaking research paper 'Cognitron: A self-organizing multilayered neural network'?
Which research paper introduced the 'Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position'?
Which research paper introduced the 'Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position'?
Who were the authors of the paper titled 'Learning representations by backpropagating errors'?
Who were the authors of the paper titled 'Learning representations by backpropagating errors'?
Who were the authors of the research paper that applied Gradient-based learning to document recognition?
Who were the authors of the research paper that applied Gradient-based learning to document recognition?
Which publication introduced 'ImageNet Classification with Deep Convolutional Neural Networks'?
Which publication introduced 'ImageNet Classification with Deep Convolutional Neural Networks'?
Which classic architecture is associated with the representation [Conv, ReLU, Pool]*N, flatten, [FC, ReLU]*N, FC, Softmax?
Which classic architecture is associated with the representation [Conv, ReLU, Pool]*N, flatten, [FC, ReLU]*N, FC, Softmax?
What is the purpose of padding in image processing?
What is the purpose of padding in image processing?
In convolution operations, what does 'Stride' refer to?
In convolution operations, what does 'Stride' refer to?
What determines the dimensions of the output feature map in convolution operations?
What determines the dimensions of the output feature map in convolution operations?
How does the input volume affect the output volume in convolutional layers?
How does the input volume affect the output volume in convolutional layers?
What is the formula for calculating the output width in convolutional operations?
What is the formula for calculating the output width in convolutional operations?
What aspect of convolutional layers is determined by the hyper-parameter 'K'?
What aspect of convolutional layers is determined by the hyper-parameter 'K'?
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Study Notes
Research Breakthroughs in CNN
- Fukushima, K. (1975) introduced the concept of "Cognitron: A self-organizing multilayered neural network" in Biological Cybernetics, 20(3-4), 121-136.
- Fukushima, K. (1980) published "Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position" in Biological Cybernetics.
- D.E.Rumelhart, G.E.Hinton, and R.J William (1986) introduced "Learning representations by backpropagating errors" in Nature, Vol 323.
Important Publications for Practical Implementation
- LeCun, Bottou, Bengio, Haffner (1998) applied gradient-based learning to document recognition, introducing LeNet-5.
- Krizhevsky, Sutskever, Hinton (2012) introduced ImageNet Classification with Deep Convolutional Neural Networks, also known as AlexNet.
Case Studies - LeNet-5 Architecture
- Classic Architecture-LeNet-5: [Conv, ReLU, Pool]*N,→ flatten,[FC, ReLU]*N, FC → Softmax
- Layer Output Size and No. of Parameters:
- Convolution-2 (f=5, k=6): 62828, 665*5
- ReLU: 62828
- MaxPool-2 (f=2, s=2): 61414
- Flatten: 5516 (120), 665*5
- Linear(120→80): 80, 120*80
Challenges for Feature Extraction
- Illumination
- Deformation
- Occlusion
- Background Clutter
- Intra-class Variation
Why Not Add More FC Layers?
- Adding more layers makes the network arbitrarily complex
- Going further deep may have issues:
- No structure
- Forcing network to memorize data instead of learning
- Optimization becomes hard (managing weights and matrix multiplications)
- Performance drops as we move further deep → Underfitting
Convolutional Neural Networks (CNNs) Introduction
- No Brain stuff
- CNNs are a special case of fully connected neural network
Convolution Operations
- Demo with stride = 1 and no padding
- Filter dimensions: 3 × 3
- Output feature map dimensions: 3 × 3
- For images with multi-channels (e.g., color images)
- Hyper-parameters in ConvLayer: Number of filters (K), Size of filters (F), the Stride (s), and amount of zero padding (P)
- Formula for output dimensions: 𝑾𝒐𝒄 = 𝑯𝒐𝒄 = 𝑾𝒊𝒏 −𝑭+𝟐𝑷 𝑺 𝑯𝒊𝒏 −𝑭+𝟐𝑷 𝑺 +𝟏 +𝟏
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