stem/AI/Neural Networks/CNN/Interpretation.md
andy 33ac3007bc vault backup: 2023-05-27 22:17:56
Affected files:
.obsidian/graph.json
.obsidian/workspace-mobile.json
.obsidian/workspace.json
STEM/AI/Neural Networks/Activation Functions.md
STEM/AI/Neural Networks/CNN/FCN/FlowNet.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/Deep Learning.md
STEM/AI/Neural Networks/MLP/Back-Propagation.md
STEM/AI/Neural Networks/MLP/MLP.md
STEM/AI/Neural Networks/Transformers/Attention.md
STEM/CS/ABI.md
STEM/CS/Calling Conventions.md
STEM/CS/Code Types.md
STEM/CS/Language Binding.md
STEM/img/am-regulariser.png
STEM/img/skip-connections.png
2023-05-27 22:17:56 +01:00

1.1 KiB

Activation Maximisation

!am.png

!am-process.png

Regulariser

  • Fit to natural image statistics
  • Prone to high frequency noise
    • Minimise
  • Total variation
x^*=\text{argmin}_{x\in \mathbb R^{H\times W\times C}}\mathcal l(\phi(x),\phi_0)
  • Won't work
x^*=\text{argmin}_{x\in \mathbb R^{H\times W\times C}}\mathcal l(\phi(x),\phi_0)+\lambda\mathcal R(x)
  • Need a regulariser like above

!am-regulariser.png

\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
\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}
  • Beta
    • Degree of smoothing