Activation Maximization

Finding the input that maximizes the activation of a Unit, mostly used to visualize the feature detectors in a CNN.

For a single neuron we can write an optimization problem like this

where

• $h$ is the Activation Function
• $x,y$ encode the position of the Unit
• $n$ is the layer
• $z$ is the channel index

The mean activation for an entire channel can be achieved by averaging over all neruons in that channel

Using the minimum instead of the maximum is not working. It will correspond to ...

You can start with an image from

• the training data
• random noise
  • need to do Regularisation like
    • jittering
    • rotation
      • linear combination of all channels leads to entangled features (makes interpretability harder)
    • scaling
    • frequency penalization to reduce variance of neighboring pixels
    • GANs
    • denoising Autoencoder