stem/AI/Neural Networks/CNN
andy d7ab8f329a vault backup: 2023-06-05 17:01:29
Affected files:
Money/Assets/Financial Instruments.md
Money/Assets/Security.md
Money/Markets/Markets.md
Politcs/Now.md
STEM/AI/Neural Networks/CNN/Examples.md
STEM/AI/Neural Networks/CNN/FCN/FCN.md
STEM/AI/Neural Networks/CNN/FCN/FlowNet.md
STEM/AI/Neural Networks/CNN/FCN/Highway Networks.md
STEM/AI/Neural Networks/CNN/FCN/ResNet.md
STEM/AI/Neural Networks/CNN/FCN/Skip Connections.md
STEM/AI/Neural Networks/CNN/FCN/Super-Resolution.md
STEM/AI/Neural Networks/CNN/GAN/DC-GAN.md
STEM/AI/Neural Networks/CNN/GAN/GAN.md
STEM/AI/Neural Networks/CNN/GAN/StackGAN.md
STEM/AI/Neural Networks/CNN/Inception Layer.md
STEM/AI/Neural Networks/CNN/Interpretation.md
STEM/AI/Neural Networks/CNN/Max Pooling.md
STEM/AI/Neural Networks/CNN/Normalisation.md
STEM/AI/Neural Networks/CNN/UpConv.md
STEM/AI/Neural Networks/CV/Layer Structure.md
STEM/AI/Neural Networks/MLP/MLP.md
STEM/AI/Neural Networks/Neural Networks.md
STEM/AI/Neural Networks/RNN/LSTM.md
STEM/AI/Neural Networks/RNN/RNN.md
STEM/AI/Neural Networks/RNN/VQA.md
STEM/AI/Neural Networks/SLP/Least Mean Square.md
STEM/AI/Neural Networks/SLP/Perceptron Convergence.md
STEM/AI/Neural Networks/SLP/SLP.md
STEM/AI/Neural Networks/Transformers/LLM.md
STEM/AI/Neural Networks/Transformers/Transformers.md
STEM/AI/Properties.md
STEM/CS/Language Binding.md
STEM/Light.md
STEM/Maths/Tensor.md
STEM/Quantum/Orbitals.md
STEM/Quantum/Schrödinger.md
STEM/Quantum/Standard Model.md
STEM/Quantum/Wave Function.md
Tattoo/Music.md
Tattoo/Plans.md
Tattoo/Sources.md
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CNN.md vault backup: 2023-06-04 22:30:39 2023-06-04 22:30:39 +01:00
Convolutional Layer.md vault backup: 2023-05-26 18:29:17 2023-05-26 18:29:17 +01:00
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Max Pooling.md vault backup: 2023-06-05 17:01:29 2023-06-05 17:01:29 +01:00
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README.md vault backup: 2023-05-31 21:29:04 2023-05-31 21:29:04 +01:00
UpConv.md vault backup: 2023-06-05 17:01:29 2023-06-05 17:01:29 +01:00

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

Dense

  • Move from convolutional operations towards vector output
  • Stochastic drop-out
    • Sub-sample channels and only connect some to dense layers

As a Descriptor

  • Most powerful as a deeply learned feature extractor
  • Dense classifier at the end isn't fantastic
    • Use SVM to classify prior to penultimate layer

cnn-descriptor

Finetuning

  • Observations
    • Most CNNs have similar weights in conv1
    • Most useful CNNs have several conv layers
      • Many weights
      • Lots of training data
    • Training data is hard to get
      • Labelling
  • Reuse weights from other network
  • Freeze weights in first 3-5 conv layers
    • Learning rate = 0
    • Randomly initialise remaining layers
    • Continue with existing weights

fine-tuning-freezing

Training

  • Validation & training loss
  • Early
    • Under-fitting
    • Training not representative
  • Later
    • Overfitting
  • V.loss can help adjust learning rate
    • Or indicate when to stop training

under-over-fitting