Finding Differences Between Transformers and ConvNets Using
Counterfactual Simulation Testing

Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing

29 November 2022

Nataniel Ruiz

Sarah Adel Bargal

Cihang Xie

Papers citing "Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing"

8 / 8 papers shown

Title
D-Feat Occlusions: Diffusion Features for Robustness to Partial Visual Occlusions in Object Recognition Rupayan Mallick Sibo Dong Nataniel Ruiz Sarah Adel Bargal DiffM 44 0 0 08 Apr 2025
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet Accuracy Kirill Vishniakov Zhiqiang Shen Zhuang Liu CLIP 14 15 0 15 Nov 2023
Hardwiring ViT Patch Selectivity into CNNs using Patch Mixing Ariel N. Lee Sarah Adel Bargal Janavi Kasera Stan Sclaroff Kate Saenko Nataniel Ruiz 18 0 0 30 Jun 2023
ModelDiff: A Framework for Comparing Learning Algorithms Harshay Shah Sung Min Park Andrew Ilyas A. Madry SyDa 25 26 0 22 Nov 2022
Are Transformers More Robust Than CNNs? Yutong Bai Jieru Mei Alan Yuille Cihang Xie ViT AAML 181 257 0 10 Nov 2021
Intriguing Properties of Vision Transformers Muzammal Naseer Kanchana Ranasinghe Salman Khan Munawar Hayat F. Khan Ming-Hsuan Yang ViT 251 618 0 21 May 2021
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions Wenhai Wang Enze Xie Xiang Li Deng-Ping Fan Kaitao Song Ding Liang Tong Lu Ping Luo Ling Shao ViT 263 3,604 0 24 Feb 2021
Densely Connected Convolutional Networks Gao Huang Zhuang Liu L. V. D. van der Maaten Kilian Q. Weinberger PINN 3DV 247 36,237 0 25 Aug 2016