CoDA-Gen: Contrastive-Optimized Diffusion Adaptation for Efficient Few-Shot Image Generation

CoDA-Gen: Contrastive-Optimized Diffusion Adaptation for Efficient Few-Shot Image Generation | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 10 March 2026 V1 Latest version Share on CoDA-Gen: Contrastive-Optimized Diffusion Adaptation for Efficient Few-Shot Image Generation Authors : Wei Ling Chua 0009-0000-7504-1358 [email protected] and Nurul Afiqah Authors Info & Affiliations https://doi.org/10.22541/au.177315483.37880791/v1 88 views 51 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Despite their success in generating high-quality images, diffusion models encounter significant challenges in data-scarce environments such as few-shot image generation (FSIG). This limitation often leads to issues like mode collapse, poor visual quality, and insufficient diversity. To address these critical problems, we propose CoDA-Gen, a novel framework for Contrastive-Optimized Diffusion Adaptation for Generative Models. CoDA-Gen integrates a lightweight, contrastiveoptimized (CoDA) module into a pre-trained diffusion U-Net, enabling efficient learning and adaptation to the unique feature distributions of a target domain from extremely few samples. Our method leverages a contrastive learning objective to align generated features with those of few-shot target samples, thereby mitigating mode collapse and enhancing both the quality and diversity of generated images. Extensive experiments on MiniImageNet, CIFAR-FS, and CelebA-HQ demonstrate that CoDA-Gen consistently outperforms state-of-the-art baselines, achieving superior FID, IS, LPIPS, and Recall scores. Furthermore, CoDA-Gen exhibits remarkable adaptation efficiency, converging rapidly and highlighting its practical utility for rapid domain specialization. Supplementary Material File (coda_gen.pdf) Download 2.15 MB Information & Authors Information Version history V1 Version 1 10 March 2026 Copyright This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License Keywords contrastive learning diffusion models domain adaptation few-shot learning image generation Authors Affiliations Wei Ling Chua 0009-0000-7504-1358 [email protected] Singapore Institute of Management View all articles by this author Nurul Afiqah Singapore Institute of Management View all articles by this author Metrics & Citations Metrics Article Usage 88 views 51 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Wei Ling Chua, Nurul Afiqah. CoDA-Gen: Contrastive-Optimized Diffusion Adaptation for Efficient Few-Shot Image Generation. Authorea . 10 March 2026. 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