2023-05-26 18:29:17 +01:00
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## Before 2010s
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- Data hungry
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- Need lots of training data
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- Processing power
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- Niche
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- No-one cared/knew about CNNs
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## After
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- ImageNet
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- 16m images, 1000 classes
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- GPUs
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- General processing GPUs
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- CUDA
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- NIPS/ECCV 2012
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- Double digit % gain on ImageNet accuracy
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# Full Connected
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Dense
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- Move from convolutional operations towards vector output
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- Stochastic drop-out
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- Sub-sample channels and only connect some to dense layers
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# As a Descriptor
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- Most powerful as a deeply learned feature extractor
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- Dense classifier at the end isn't fantastic
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- Use SVM to classify prior to penultimate layer
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![[cnn-descriptor.png]]
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# Finetuning
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- Observations
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- Most CNNs have similar weights in conv1
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- Most useful CNNs have several conv layers
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- Many weights
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- Lots of training data
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- Training data is hard to get
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- Labelling
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- Reuse weights from other network
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- Freeze weights in first 3-5 conv layers
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- Learning rate = 0
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- Randomly initialise remaining layers
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- Continue with existing weights
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![[fine-tuning-freezing.png]]
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# Training
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- Validation & training loss
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- Early
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- Under-fitting
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- Training not representative
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- Later
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- Overfitting
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- V.loss can help adjust learning rate
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- Or indicate when to stop training
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![[under-over-fitting.png]]
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