3 accepted papers
Prototype-based methods enhance interpretability in image recognition by establishing intermediate part-level prototypes to build interpretable classifiers, enabling transparent decision-making through localized evidence and reference to prototypical examples. However, most existing methods typicall
Prototypical Part Networks facilitate interpretable decision-making processes, with the classification score computed by comparing test image patches to learned prototypes. Existing work typically learns prototypes from Convolutional Neural Networks (CNNs). However, learning prototypical parts direc…
DNN-based super-resolution(SR) models inherit the black-box nature of DNN and present low transparency. However, few works focus on interpreting low-level SR models and the limited existing gradient-based interpretability methods often require large computational costs and produce spurious/noisy fea…