Fig. 2: Representational geometries at different network depths of two DNN instances.
From: Individual differences among deep neural network models
The internal representations of two network instances were characterized based on their representational geometries. We computed the pairwise distances (correlation distance) between activity patterns in response to 1000 test stimuli from 10 visual categories and visualized them in 2D via multidimensional scaling (MDS; metric stress criterion; categories shown in different colors). With increasing depth, networks exhibit increased category clustering.
