Prototype Generation: Robust Feature Visualisation for Data Independent
  Interpretability
Arush Tagade Jessica Rumbelow
We introduce Prototype Generation, a stricter and more robust form of feature
visualisation for model-agnostic, data-independent interpretability of image
classification models. We demonstrate its ability to generate inputs that
result in natural activation paths, countering previous claims that feature
visualisation algorithms are untrustworthy due to the unnatural internal
activations. We substantiate these claims by quantitatively measuring
similarity between the internal activations of our generated prototypes and
natural images. We also demonstrate how the interpretation of generated
prototypes yields important insights, highlighting spurious correlations and
biases learned by models which quantitative methods over test-sets cannot
identify.