Part3: Dataset Exemplars

[nuna-aa]

This notebook implements a comprehensive investigation into neural network interpretability using the InceptionV1 architecture. The experiment combines two powerful techniques:

Feature optimization using gradient ascent to synthesize inputs that maximally activate specific neurons, and
Dataset exemplar identification to find real-world images that naturally trigger strong neural responses. The methodology follows principles from Distill.pub's neural network interpretability research, creating visualizations that reveal what individual neurons have learned to detect.