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Affected files: STEM/AI/Neural Networks/CNN/Examples.md STEM/AI/Neural Networks/CNN/FCN/FCN.md STEM/AI/Neural Networks/CNN/FCN/ResNet.md STEM/AI/Neural Networks/CNN/FCN/Skip Connections.md STEM/AI/Neural Networks/CNN/GAN/DC-GAN.md STEM/AI/Neural Networks/CNN/GAN/GAN.md STEM/AI/Neural Networks/CNN/Interpretation.md STEM/AI/Neural Networks/CNN/UpConv.md STEM/AI/Neural Networks/Deep Learning.md STEM/AI/Neural Networks/MLP/MLP.md STEM/AI/Neural Networks/Properties+Capabilities.md STEM/AI/Neural Networks/SLP/Least Mean Square.md STEM/AI/Neural Networks/SLP/SLP.md STEM/AI/Neural Networks/Transformers/Transformers.md STEM/AI/Properties.md STEM/CS/Language Binding.md STEM/CS/Languages/dotNet.md STEM/Signal Proc/Image/Image Processing.md
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- Fractionally strided convolution
- Transposed Convolution
- Like a deep interpolation
- Convolution with a fractional input stride
- Up-sampling is convolution 'in reverse'
- Not an actual inverse convolution
- For scaling up by a factor of
f- Consider as a Convolution of stride
1/f
- Consider as a Convolution of stride
- Could specify kernel
- Or learn
- Can have multiple upconv layers
- Separated by ReLu
- For non-linear up-sampling conv
- Interpolation is linear
Convolution Matrix
Normal
- Equivalent operation with a flattened input
- Row per kernel location
- Many-to-one operation
Understanding transposed convolutions
Transposed
- One-to-many
