Prion-ViT: Prions-Inspired Vision Transformers for Temperature prediction with Specklegrams
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913666340749312 |
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| author | Sebastian, Abhishek R, Pragna Rajagopal, Sonaa Mani, Muralikrishnan |
| author_facet | Sebastian, Abhishek R, Pragna Rajagopal, Sonaa Mani, Muralikrishnan |
| contents | Fiber Specklegram Sensors (FSS) are vital for environmental monitoring due to their high temperature sensitivity, but their complex data poses challenges for predictive models. This study introduces Prion-ViT, a prion-inspired Vision Transformer model, inspired by biological prion memory mechanisms, to improve long-term dependency modeling and temperature prediction accuracy using FSS data. Prion-ViT leverages a persistent memory state to retain and propagate key features across layers, reducing mean absolute error (MAE) to 0.71$^\circ$C and outperforming models like ResNet, Inception Net V2, and Standard Vision Transformers. This paper also discusses Explainable AI (XAI) techniques, providing a perspective on specklegrams through attention and saliency maps, which highlight key regions contributing to predictions |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05836 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prion-ViT: Prions-Inspired Vision Transformers for Temperature prediction with Specklegrams Sebastian, Abhishek R, Pragna Rajagopal, Sonaa Mani, Muralikrishnan Computer Vision and Pattern Recognition Signal Processing Optics Fiber Specklegram Sensors (FSS) are vital for environmental monitoring due to their high temperature sensitivity, but their complex data poses challenges for predictive models. This study introduces Prion-ViT, a prion-inspired Vision Transformer model, inspired by biological prion memory mechanisms, to improve long-term dependency modeling and temperature prediction accuracy using FSS data. Prion-ViT leverages a persistent memory state to retain and propagate key features across layers, reducing mean absolute error (MAE) to 0.71$^\circ$C and outperforming models like ResNet, Inception Net V2, and Standard Vision Transformers. This paper also discusses Explainable AI (XAI) techniques, providing a perspective on specklegrams through attention and saliency maps, which highlight key regions contributing to predictions |
| title | Prion-ViT: Prions-Inspired Vision Transformers for Temperature prediction with Specklegrams |
| topic | Computer Vision and Pattern Recognition Signal Processing Optics |
| url | https://arxiv.org/abs/2411.05836 |