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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.18286 |
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| _version_ | 1866914875835416576 |
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| author | Kazimi, Bashir Ruzaeva, Karina Sandfeld, Stefan |
| author_facet | Kazimi, Bashir Ruzaeva, Karina Sandfeld, Stefan |
| contents | In this work, we explore the potential of self-supervised learning with Generative Adversarial Networks (GANs) for electron microscopy datasets. We show how self-supervised pretraining facilitates efficient fine-tuning for a spectrum of downstream tasks, including semantic segmentation, denoising, noise \& background removal, and super-resolution. Experimentation with varying model complexities and receptive field sizes reveals the remarkable phenomenon that fine-tuned models of lower complexity consistently outperform more complex models with random weight initialization. We demonstrate the versatility of self-supervised pretraining across various downstream tasks in the context of electron microscopy, allowing faster convergence and better performance. We conclude that self-supervised pretraining serves as a powerful catalyst, being especially advantageous when limited annotated data are available and efficient scaling of computational cost is important. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18286 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy Kazimi, Bashir Ruzaeva, Karina Sandfeld, Stefan Computer Vision and Pattern Recognition Materials Science Artificial Intelligence Machine Learning In this work, we explore the potential of self-supervised learning with Generative Adversarial Networks (GANs) for electron microscopy datasets. We show how self-supervised pretraining facilitates efficient fine-tuning for a spectrum of downstream tasks, including semantic segmentation, denoising, noise \& background removal, and super-resolution. Experimentation with varying model complexities and receptive field sizes reveals the remarkable phenomenon that fine-tuned models of lower complexity consistently outperform more complex models with random weight initialization. We demonstrate the versatility of self-supervised pretraining across various downstream tasks in the context of electron microscopy, allowing faster convergence and better performance. We conclude that self-supervised pretraining serves as a powerful catalyst, being especially advantageous when limited annotated data are available and efficient scaling of computational cost is important. |
| title | Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy |
| topic | Computer Vision and Pattern Recognition Materials Science Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2402.18286 |