andy
d7ab8f329a
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
35 lines
1.1 KiB
Markdown
35 lines
1.1 KiB
Markdown
- Fractionally strided convolution
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- Transposed [Convolution](../../../Signal%20Proc/Convolution.md)
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- Like a deep interpolation
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- Convolution with a fractional input stride
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- Up-sampling is convolution 'in reverse'
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- Not an actual inverse convolution
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- For scaling up by a factor of $f$
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- Consider as a [Convolution](../../../Signal%20Proc/Convolution.md) of stride $1/f$
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- Could specify kernel
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- Or learn
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- Can have multiple upconv layers
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- Separated by [[Activation Functions#ReLu|ReLu]]
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- For non-linear up-sampling conv
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- Interpolation is linear
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# Convolution Matrix
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Normal
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- Equivalent operation with a flattened input
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- Row per kernel location
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- Many-to-one operation
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[Understanding transposed convolutions](https://www.machinecurve.com/index.php/2019/09/29/understanding-transposed-convolutions/)
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## Transposed
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- One-to-many
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