Is Attentional Channel Processing Design Required? Comprehensive Analysis Of Robustness Between Vision Transformers And Fully Attentional Networks
Not on the map. Why?
Abstract (arXiv)
The robustness testing has been performed for standard CNN models and Vision Transformers, however there is a lack of comprehensive study between the robustness of traditional Vision Transformers without an extra attentional channel design and the latest fully attentional network(FAN) models. So in this paper, we use the ImageNet dataset to compare the robustness of fully attentional network(FAN) models with traditional Vision Transformers to understand the role of an attentional channel processing design using white box attacks and also study the transferability between the same using black box attacks.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 5 references that are in the map. First posted on arXiv 8 Jun 2023.
Builds on
5 of 5 references.
References · 5
- 2021Understanding Robustness of Transformers for Image Classificationcited by 250
- 2021On the Robustness of Vision Transformers to Adversarial Examplescited by 167
- 2022Understanding The Robustness in Vision Transformerscited by 144
- 2018Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Modelscited by 135
- 2022Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?cited by 47
Built on by
Not cited yet.