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Convolutional Neural Networks Lecture
Briefing
What is a Convolutional Network
- Convolutional Layers
- \(3\times3\) or \(5\times5\) (possibly \(7\times7\))
- Pooling
- Often maximum; sometimes average or other functions
- Convolution-Detector-Pooling = Convolutional Unit
- Convolution
- Activation (Detector)
- Pooling
Why is convolution suitable for images?
Features of CNN
- Capacity (degrees of freedom)
- fewer weights (DOF) per layer
- Representation learning
- may learn edge or corner detection for instance
- Location invariance
- Hierarchies; serial and parallel modularisation
- Fully connected layer at the end.
- convolutional units learn features
- last layer uses the features like a traditional ANN with one hidden layer
Considerations in CNN
- Padding (edge conditions for convolution)
- Batch Normalisation
- mini batches
- Filter/Kernel
Other important features (any ANN)
- Normalisation
- Over- and under-training
- Plot over epochs
- Feature detection