andy
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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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FCN.md | ||
FlowNet.md | ||
Highway Networks.md | ||
README.md | ||
ResNet.md | ||
Skip Connections.md | ||
Super-Resolution.md |
Fully Convolutional Network
Convolutional Layer and UpConv with Activation Functions#ReLu but no others (pooling)
- All some sort of Encoder-Decoder
Contractive → UpConv
Image Segmentation
- For visual output
- Previously image
\rightarrow
vector
- Previously image
- Additional layers to up-sample representation to an image
- Up-convolutional
- De-convolutional
Training
- Rarely from scratch
- Pre-trained weights
- Replace final layers
- MLP layers
- White-noise initialised
- Add UpConv layer(s)
- Fine-tune train
- Freeze others
- Annotated GT images
- Can use summed per-pixel log Deep Learning#Loss Function
Evaluation
- SDS
- Classical method
- 52% mAP
- FCN
- 62% mAP
- Intersection over Union
- IOU
- Jaccard
- Averaged over all images
J(A,B)=\frac{|A\cap B|}{|A\cup B|}