T-TAME: Trainable Attention Mechanism for Explaining Convolutional Networks and Vision Transformers

arXiv:2403.04523v1 Announce Type: cross
Abstract: 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 networks is a barrier to adoption in applications where explainability is essential. While some techniques for generating explanations have been proposed, primarily for Convolutional Neural Networks, adapting such techniques to the new paradigm of Vision Transformers is non-trivial. This paper presents T-TAME, Transformer-compatible Trainable Attention Mechanism for Explanations, a general …

abstract adoption applications architectures arxiv attention black box box classification cs.ai cs.cv cs.lg cs.mm development explainability however image nature networks neural networks tasks transformers type vision vision transformers