T-TAME: Trainable Attention Mechanism for Explaining Convolutional Networks and Vision Transformers
This addresses the need for explainability in deep learning for applications where transparency is essential, offering a general and efficient solution for both convolutional networks and Vision Transformers.
The paper tackles the problem of explaining deep neural networks for image classification by introducing T-TAME, a trainable attention mechanism that generates explanation maps in a single forward pass, achieving state-of-the-art performance comparable to or better than computationally expensive methods on architectures like VGG-16, ResNet-50, and ViT-B-16 trained on ImageNet.
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.