Summary of T-tame: Trainable Attention Mechanism For Explaining Convolutional Networks and Vision Transformers, by Mariano V. Ntrougkas et al.
T-TAME: Trainable Attention Mechanism for Explaining Convolutional Networks and Vision Transformers
by Mariano V. Ntrougkas, Nikolaos Gkalelis, Vasileios Mezaris
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The rapid development and adoption of Vision Transformers for image classification tasks have been hindered by the “black box” nature of neural networks, which makes them unsuitable for applications where explainability is essential. To address this issue, researchers have proposed techniques for generating explanations for Convolutional Neural Networks, but adapting these techniques to Vision Transformers is non-trivial. This paper presents T-TAME, 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, achieving SOTA performance. This paper demonstrates improvements over existing state-of-the-art explainability methods by applying T-TAME to three popular deep learning classifier architectures (VGG-16, ResNet-50, and ViT-B-16) trained on the ImageNet dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a super smart computer that can recognize pictures. But sometimes we want to know why it made certain decisions, like what features of an image are most important for recognition. This is called “explainability” and it’s crucial in many real-world applications. The problem is that these computers (called neural networks) are often too complex for us to understand how they work. Researchers have been working on solving this issue by developing techniques to explain the decisions made by these computers. In this paper, scientists propose a new method called T-TAME that can be used with any of these computer vision models to generate explanations for their decisions. The results show that T-TAME outperforms existing methods and provides valuable insights into how these computers work. |
Keywords
* Artificial intelligence * Deep learning * Image classification * Neural network * Resnet * Vision transformer * Vit
