stem/AI/Neural Networks/CNN/Interpretation.md

Activation Maximisation

• Synthesise an ideal image for a class
  • Maximise 1-hot output
  • Maximise Activation Functions#SoftMax

!

• Use trained network
  • Don't update weights
• Architectures noise
  • Back-Propagation Deep Learning#Loss Function
    • Don't update weights
    • Update image

!

Regulariser

• Fit to natural image statistics
• Prone to high frequency noise
  • Minimise
• Total variation
  • x^* is the best solution to minimise Deep Learning#Loss Function

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

!

\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