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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
41 lines
1.1 KiB
Markdown
41 lines
1.1 KiB
Markdown
Fully [Convolution](../../../../Signal%20Proc/Convolution.md)al Network
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[[Convolutional Layer|Convolutional]] and [[UpConv|up-convolutional layers]] with [[Activation Functions#ReLu|ReLu]] but no others (pooling)
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- All some sort of Encoder-Decoder
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Contractive → [UpConv](../UpConv.md)
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# Image Segmentation
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- For visual output
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- Previously image $\rightarrow$ vector
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- Additional layers to up-sample representation to an image
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- Up-[convolution](../../../../Signal%20Proc/Convolution.md)al
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- De-[convolution](../../../../Signal%20Proc/Convolution.md)al
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![fcn-uses](../../../../img/fcn-uses.png)
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![fcn-arch](../../../../img/fcn-arch.png)
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# Training
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- Rarely from scratch
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- Pre-trained weights
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- Replace final layers
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- [[MLP|FC]] layers
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- White-noise initialised
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- Add [UpConv](../UpConv.md) layer(s)
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- Fine-tune train
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- Freeze others
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- Annotated GT images
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- Can use summed per-pixel log [[Deep Learning#Loss Function|loss]]
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# Evaluation
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![fcn-eval](../../../../img/fcn-eval.png)
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- SDS
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- Classical method
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- 52% mAP
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- FCN
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- 62% mAP
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- Intersection over Union
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- IOU
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- Jaccard
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- Averaged over all images
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- $J(A,B)=\frac{|A\cap B|}{|A\cup B|}$ |