Out of Distribution Performance of State of Art Vision Model

• URL: http://arxiv.org/abs/2301.10750v3
• Date: Sun, 8 Oct 2023 22:01:52 GMT
• Title: Out of Distribution Performance of State of Art Vision Model
• Authors: Salman Rahman and Wonkwon Lee
• Abstract summary: ViT's self-attention mechanism, according to the claim, makes it more robust than CNN. We investigate the performance of 58 state-of-the-art computer vision models in a unified training setup.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The vision transformer (ViT) has advanced to the cutting edge in the visual recognition task. Transformers are more robust than CNN, according to the latest research. ViT's self-attention mechanism, according to the claim, makes it more robust than CNN. Even with this, we discover that these conclusions are based on unfair experimental conditions and just comparing a few models, which did not allow us to depict the entire scenario of robustness performance. In this study, we investigate the performance of 58 state-of-the-art computer vision models in a unified training setup based not only on attention and convolution mechanisms but also on neural networks based on a combination of convolution and attention mechanisms, sequence-based model, complementary search, and network-based method. Our research demonstrates that robustness depends on the training setup and model types, and performance varies based on out-of-distribution type. Our research will aid the community in better understanding and benchmarking the robustness of computer vision models.

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