andy
236a5eac06
Affected files: .obsidian/app.json .obsidian/appearance.json .obsidian/workspace.json Money/Markets/Commodity.md STEM/AI/Neural Networks/CNN/CNN.md STEM/AI/Neural Networks/CNN/GAN/CycleGAN.md STEM/AI/Neural Networks/CNN/GAN/DC-GAN.md STEM/AI/Neural Networks/CNN/GAN/cGAN.md STEM/AI/Neural Networks/CV/Data Manipulations.md STEM/AI/Neural Networks/MLP/Back-Propagation.md STEM/CS/Code Types.md STEM/CS/Compilers.md STEM/Quantum/Confinement.md
54 lines
1.6 KiB
Markdown
54 lines
1.6 KiB
Markdown
## 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](../CV/Datasets.md#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](../CV/Datasets.md#ImageNet) accuracy
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# Full Connected
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[Dense](../MLP/MLP.md)
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- Move from [convolutional](Convolutional%20Layer.md) operations towards vector output
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- Stochastic drop-out
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- Sub-sample channels and only connect some to [dense](../MLP/MLP.md) 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](../MLP/MLP.md) classifier at the end isn't fantastic
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- Use SVM to classify prior to penultimate layer
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# Finetuning
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- Observations
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- Most CNNs have similar weights in [conv1](Convolutional%20Layer.md)
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- Most useful CNNs have several [conv layers](Convolutional%20Layer.md)
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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](Convolutional%20Layer.md)
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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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# Training
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- Validation & training [loss](../Deep%20Learning.md#Loss Function)
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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](../Deep%20Learning.md#Loss Function) can help adjust learning rate
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- Or indicate when to stop training
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