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Affected files: .obsidian/global-search.json .obsidian/workspace.json Health/Alexithymia.md Health/BWS.md STEM/AI/Neural Networks/Activation Functions.md STEM/AI/Neural Networks/Architectures.md STEM/AI/Neural Networks/CNN/CNN.md STEM/AI/Neural Networks/MLP/Back-Propagation.md STEM/AI/Neural Networks/Transformers/Attention.md STEM/CS/Calling Conventions.md STEM/CS/Languages/Assembly.md |
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FCN | ||
GAN | ||
CNN.md | ||
Convolutional Layer.md | ||
Examples.md | ||
Inception Layer.md | ||
Interpretation.md | ||
Max Pooling.md | ||
Normalisation.md | ||
README.md | ||
UpConv.md |
Before 2010s
- Data hungry
- Need lots of training data
- Processing power
- Niche
- No-one cared/knew about CNNs
After
- ImageNet
- 16m images, 1000 classes
- GPUs
- General processing GPUs
- CUDA
- NIPS/ECCV 2012
- Double digit % gain on ImageNet accuracy
Full Connected
- Move from Convolutional Layer operations towards vector output
- Stochastic drop-out
- Sub-sample channels and only connect some to MLP layers
As a Descriptor
- Most powerful as a deeply learned feature extractor
- MLP classifier at the end isn't fantastic
- Use SVM to classify prior to penultimate layer
Finetuning
- Observations
- Most CNNs have similar weights in Convolutional Layer
- Most useful CNNs have several Convolutional Layer
- Many weights
- Lots of training data
- Training data is hard to get
- Labelling
- Reuse weights from other network
- Freeze weights in first 3-5 Convolutional Layer
- Learning rate = 0
- Randomly initialise remaining layers
- Continue with existing weights
Training
- Validation & training Deep Learning#Loss Function
- Early
- Under-fitting
- Training not representative
- Later
- Overfitting
- V.Deep Learning#Loss Function can help adjust learning rate
- Or indicate when to stop training