# TAME: Attention Mechanism Based Feature Fusion for Generating Explanation Maps of Convolutional Neural Networks Source: [TAME: Attention Mechanism Based Feature Fusion for Generating Explanation Maps of Convolutional Neural Networks]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:02:16.872Z Authors: Mariano Ntrougkas, Nikolaos Gkalelis, Vasileios Mezaris Published: 2023-01-18T10:05:28.000Z ## Abstract 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 Mechanism for Explanations), a method for generating explanation maps with a multi-branch hierarchical attention mechanism. TAME combines a target model's feature maps from multiple layers using an attention mechanism, transforming them into an explanation map. TAME can easily be applied to any convolutional neural network (CNN) by streamlining the optimization of the attention mechanism's training method and the selection of target model's feature maps. After training, explanation maps can be computed in a single forward pass. We apply TAME to two widely used models, i.e. VGG-16 and ResNet-50, trained on ImageNet and show improvements over previous top-performing methods. We also provide a comprehensive ablation study comparing the performance of different variations of TAME's architecture. TAME source code is made publicly available at https://github.com/bmezaris/TAME ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()