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cs.CV2025
Inspecting Training Dynamics of Similarity Development in Supervised Vision Networks
Katarzyna Filus, Mateusz Żarski
For trustworthy and human-aware artificial intelligence, models should be evaluated beyond accuracy, among others through error predictability and semantic alignment. Similarity is…
cs.CV2025
Semantic Depth Matters: Explaining Errors of Deep Vision Networks through Perceived Class Similarities
Katarzyna Filus, Michał Romaszewski, Mateusz Żarski
Understanding deep neural network (DNN) behavior requires more than evaluating classification accuracy alone; analyzing errors and their predictability is equally crucial. Current…