{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/f-ancgan-an-attention-enhanced-cycle","title":"F-ANcGAN: An Attention-Enhanced Cycle Consistent Generative Adversarial Architecture for Synthetic Image Generation of Nanoparticles","arxiv_id":"2505.18106","date":"2025-05-23","proceeding":null,"authors":["Varun Ajith","Anindya Pal","Saumik Bhattacharya","Sayantari Ghosh"],"abstract":"Nanomaterial research is becoming a vital area for energy, medicine, and materials science, and accurate analysis of the nanoparticle topology is essential to determine their properties. Unfortunately, the lack of high-quality annotated datasets drastically hinders the creation of strong segmentation models for nanoscale imaging. To alleviate this problem, we introduce F-ANcGAN, an attention-enhanced cycle consistent generative adversarial system that can be trained using a limited number of data samples and generates realistic scanning electron microscopy (SEM) images directly from segmentation maps. Our model uses a Style U-Net generator and a U-Net segmentation network equipped with self-attention to capture structural relationships and applies augmentation methods to increase the variety of the dataset. The architecture reached a raw FID score of 17.65 for TiO$_2$ dataset generation, with a further reduction in FID score to nearly 10.39 by using efficient post-processing techniques. By facilitating scalable high-fidelity synthetic dataset generation, our approach can improve the effectiveness of downstream segmentation task training, overcoming severe data shortage issues in nanoparticle analysis, thus extending its applications to resource-limited fields.","url_abs":"https://arxiv.org/abs/2505.18106v1","url_pdf":"https://arxiv.org/pdf/2505.18106v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"f-ancgan-an-attention-enhanced-cycle","repo_url":"https://github.com/Pal-kid404/F-ANcGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"dataset-generation","task_name":"Dataset Generation"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-generation-on-tio-2-nanoparticle","task":"Image Generation","dataset":"TiO_2 nanoparticle","model":"F-ANcGAN","rank_in_archive_order":1,"of":3,"metrics":{"FID":"17.65"},"uses_additional_data":false},{"leaderboard":"/sota/image-generation-on-tio-2-nanoparticle","task":"Image Generation","dataset":"TiO_2 nanoparticle","model":"Cycle GAN","rank_in_archive_order":2,"of":3,"metrics":{"FID":"52.01"},"uses_additional_data":false},{"leaderboard":"/sota/image-generation-on-tio-2-nanoparticle","task":"Image Generation","dataset":"TiO_2 nanoparticle","model":"Generative Adversarial Network (GAN)","rank_in_archive_order":3,"of":3,"metrics":{"FID":"69.9"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}