# Understanding How Image Quality Affects Transformer Neural Networks
## 5 Conclusions  
In this study, we evaluated the robustness of 22 state-of-the-art transformer models to five different types of image distortion: additive Gaussian noise, multiplicative Gaussian noise, Gaussian blur, salt-and-pepper noise, and JPEG compression noise. Our comprehensive analysis provides critical insights into how well these advanced models can maintain performance in the presence of various common distortions. Our results indicate a notable variance in robustness across the transformer models, with some models exhibiting significant resilience to certain noise types while others showed considerable degradation in performance. Additive and multiplicative Gaussian noise were particularly challenging for many models, leading to substantial performance drops. Conversely, the models handled JPEG compression artifacts very effectively. These findings highlight the importance of incorporating noise-specific augmentation techniques during the training phase to bolster model robustness. Future work should explore targeted training methodologies, incorporating a broader range of noise types and real-world distortions, to develop models that are not only accurate but also reliable in diverse and unpredictable environments. In summary, our study provides a valuable benchmark for the robustness of transformer models to image noise, offering guidance for future research and development in creating  
Figure 10: Top-5 accuracy rates under different JPEG distortions. (**a**) SwiftFormer-XS, SwiftFormer-S, SwiftFormer-L1, SwiftFormer-L3, VAN-B6. (**b**) CVT-13, CVT-21, CVT-W24, EfficientFormer-L1, EfficientFormer-L3, EfficientFormer-L7. (**c**) Swin-S, Swin-B, Swin-L, MobileViT-XS, MobileViT-XS, MobileViT-S. (**d**) ViT-B/16, ViT-L/16, LeViT-128, LeViT-256, LeViT-384. more resilient computer vision systems. By addressing the vulnerabilities identified in this analysis, we can enhance the practical applicability of these models in real-world scenarios where image quality can often be compromised. In future work, we plan to extend our exploration of noise robustness to other computer vision tasks beyond image classification, including object detection, segmentation, and image generation. Additionally, it would be valuable to examine the practical implications of our findings, such as the development of noise-aware model selection or deployment strategies for real-world applications. This research received no external funding. The data used in this study are available for download at [[https://huggingface.co/datasets/imagenet-1k](https://huggingface.co/datasets/imagenet-1k)]([https://huggingface.co/datasets/imagenet-1k](https://huggingface.co/datasets/imagenet-1k)) (accessed on 1 August 2024). The experimental and numerical results supporting the findings of this study are available at [[https://github.com/elektrische-schafen/Understanding-How-Image-Quality-Affects-Transformer-Neural-Networks](https://github.com/elektrische-schafen/Understanding-How-Image-Quality-Affects-Transformer-Neural-Networks)]([https://github.com/elektrische-schafen/Understanding-How-Image-Quality-Affects-Transformer-Neural-Networks](https://github.com/elektrische-schafen/Understanding-How-Image-Quality-Affects-Transformer-Neural-Networks)) (accessed on 1 August 2024). We would like to express our sincere gratitude to our colleague [PERSON] for his invaluable assistance and expertise in GPU computing. His guidance and support have been instrumental in optimizing our computational workflows and accelerating the progress of this research project. We would like to express our heartfelt gratitude to the entire team of Nokia Bell Labs, Budapest for fostering an environment of collaboration, support, and positivity throughout the duration of this project. We thank the anonymous reviewers and the academic editor for their careful reading of our manuscript and their many insightful comments and suggestions. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: