IEEE International Geoscience and Remote Sensing Symposium / 2023

Master GAN: Multiple Attention is all you Need: A Multiple Attention Guided Super Resolution Network for Dems

Azhan Mohammed, Mohammad Kashif, Md Haider Zama, Mohammed Abbas Ansari, Saquib Ali

Generative AIGraph LearningPopular and Landmark Papers

The task of transforming low-resolution remote sensing images to high-resolution has consistently presented a formidable challenge in the field. The use of Generative Adversarial Networks (GANs) has led to tremendous development in the field. In this study, a novel super resolution architecture Multiple Attention Swin Transformer Enhanced Residual GAN (MASTER GAN) has been introduced, that uses multiple attention techniques in a neural network trained in an adversarial training environment. The introduced MASTER GAN acheives state-of-the-art results in super resolution tasks, when compared to existing mechanism. The paper also introduces an open source database of low resolution and counter high resolution imagery, generated using Kernel GAN. The training code has been provided at: https://github.com/sheikhazhanmohammed/MASTERGAN.git

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Title: Master GAN: Multiple Attention is all you Need: A Multiple Attention Guided Super Resolution Network for Dems
Authors: Azhan Mohammed, Mohammad Kashif, Md Haider Zama, Mohammed Abbas Ansari, Saquib Ali
Year: 2023
Venue: IEEE International Geoscience and Remote Sensing Symposium
Categories: Generative AI, Graph Learning, Popular and Landmark Papers
Citations: 6
Paper URL: https://www.semanticscholar.org/paper/a972d28273fe7e9c7608a562449719ffa08f769f
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Abstract:
The task of transforming low-resolution remote sensing images to high-resolution has consistently presented a formidable challenge in the field. The use of Generative Adversarial Networks (GANs) has led to tremendous development in the field. In this study, a novel super resolution architecture Multiple Attention Swin Transformer Enhanced Residual GAN (MASTER GAN) has been introduced, that uses multiple attention techniques in a neural network trained in an adversarial training environment. The introduced MASTER GAN acheives state-of-the-art results in super resolution tasks, when compared to existing mechanism. The paper also introduces an open source database of low resolution and counter high resolution imagery, generated using Kernel GAN. The training code has been provided at: https://github.com/sheikhazhanmohammed/MASTERGAN.git

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- Google Scholar: Google Scholar references (https://scholar.google.com/scholar?q=Master%20GAN%3A%20Multiple%20Attention%20is%20all%20you%20Need%3A%20A%20Multiple%20Attention%20Guided%20Super%20Resolution%20Network%20for%20Dems)
- Papers with Code: Papers with Code search (https://paperswithcode.com/search?q=Master%20GAN%3A%20Multiple%20Attention%20is%20all%20you%20Need%3A%20A%20Multiple%20Attention%20Guided%20Super%20Resolution%20Network%20for%20Dems)
- Semantic Scholar: Semantic Scholar paper page (https://www.semanticscholar.org/paper/a972d28273fe7e9c7608a562449719ffa08f769f)
- YouTube: YouTube explanations (https://www.youtube.com/results?search_query=Master%20GAN%3A%20Multiple%20Attention%20is%20all%20you%20Need%3A%20A%20Multiple%20Attention%20Guided%20Super%20Resolution%20Network%20for%20Dems+paper+explained)