9 citations · 10 across the 2 of their papers we have counts for
3 papers
P-TAME: Explain Any Image Classifier with Trained Perturbations
Mariano V. Ntrougkas, Vasileios Mezaris, Ioannis Patras
The adoption of Deep Neural Networks (DNNs) in critical fields where predictions need to be accompanied by justifications is hindered by their inherent black-box nature. In this pa…
T-TAME: Trainable Attention Mechanism for Explaining Convolutional Networks and Vision Transformers
Mariano V. Ntrougkas, Nikolaos Gkalelis, Vasileios Mezaris
The development and adoption of Vision Transformers and other deep-learning architectures for image classification tasks has been rapid. However, the "black box" nature of neural n…
TAME: Attention Mechanism Based Feature Fusion for Generating Explanation Maps of Convolutional Neural Networks
Mariano Ntrougkas, Nikolaos Gkalelis, Vasileios Mezaris
The apparent ``black box'' nature of neural networks is a barrier to adoption in applications where explainability is essential. This paper presents TAME (Trainable Attention Mecha…