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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
33 lines
1.3 KiB
Markdown
33 lines
1.3 KiB
Markdown
# Activation Maximisation
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- Synthesise an ideal image for a class
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- Maximise 1-hot output
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- Maximise [SoftMax](../Activation%20Functions.md#SoftMax)
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- **Use trained network**
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- Don't update weights
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- [Feedforward](../Architectures.md) noise
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- [Back-propagate](../MLP/Back-Propagation.md) [loss](../Deep%20Learning.md#Loss%20Function)
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- Don't update weights
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- Update image
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## Regulariser
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- Fit to natural image statistics
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- Prone to high frequency noise
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- Minimise
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- Total variation
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- $x^*$ is the best solution to minimise [loss](../Deep%20Learning.md#Loss%20Function)
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$$x^*=\text{argmin}_{x\in \mathbb R^{H\times W\times C}}\mathcal l(\phi(x),\phi_0)$$
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- Won't work
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$$x^*=\text{argmin}_{x\in \mathbb R^{H\times W\times C}}\mathcal l(\phi(x),\phi_0)+\lambda\mathcal R(x)$$
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- Need a regulariser like above
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$$\mathcal R_{V^\beta}(f)=\int_\Omega\left(\left(\frac{\partial f}{\partial u}(u,v)\right)^2+\left(\frac{\partial f}{\partial v}(u,v)\right)^2\right)^{\frac \beta 2}du\space dv$$
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$$\mathcal R_{V^\beta}(x)=\sum_{i,j}\left(\left(x_{i,j+1}-x_{ij}\right)^2+\left(x_{i+1,j}-x_{ij}\right)^2\right)^{\frac \beta 2}$$
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- Beta
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- Degree of smoothing |