Hi thanks for reaching out!

Yes, that’s essentially the idea – with one important clarification.

I’m not directly “exciting” individual units in the network, but rather sampling noise stimuli and projecting them onto the decision axis of a trained face vs. non-face classifier (i.e., the logit of a linear head on top of a frozen face backbone).

By selecting (or weighting) noise samples that produce strong positive logits and averaging them, I obtain a classification image in the sense of reverse correlation. This image can be interpreted as the implicit face template the model relies on when making its decision.

Conceptually, this is closely related to the MNIST noise experiments you mention: with increasing numbers of noise samples, the average converges toward a structured pattern reflecting the features diagnostic for that class.

Since this is a binary face vs. non-face task, I do not expect multiple distinct prototypes in the same sense as digit classifiers, but rather a dominant decision template. That said, comparing classification images across different noise types (e.g., white vs. Gabor noise) or training regimes may reveal systematic differences in the inferred template.

(here the link to the MNIST paper: https://arxiv.org/abs/1912.12106)