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

• URL: http://arxiv.org/abs/2403.04523v1
• Date: Thu, 7 Mar 2024 14:25:03 GMT
• Title: T-TAME: Trainable Attention Mechanism for Explaining Convolutional Networks and Vision Transformers
• Authors: Mariano V. Ntrougkas, Nikolaos Gkalelis, Vasileios Mezaris
• Abstract summary: "Black box" nature of neural networks is a barrier to adoption in applications where explainability is essential. This paper presents T-TAME, Transformer-compatible Trainable Attention Mechanism for Explanations. Proposed architecture and training technique can be easily applied to any convolutional or Vision Transformer-like neural network.
• Score: 9.284740716447342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 methodology for explaining deep neural networks used in image classification tasks. The proposed architecture and training technique can be easily applied to any convolutional or Vision Transformer-like neural network, using a streamlined training approach. After training, explanation maps can be computed in a single forward pass; these explanation maps are comparable to or outperform the outputs of computationally expensive perturbation-based explainability techniques, achieving SOTA performance. We apply T-TAME to three popular deep learning classifier architectures, VGG-16, ResNet-50, and ViT-B-16, trained on the ImageNet dataset, and we demonstrate improvements over existing state-of-the-art explainability methods. A detailed analysis of the results and an ablation study provide insights into how the T-TAME design choices affect the quality of the generated explanation maps.

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