{"slug": "mapping-networks-cvpr-2026-best-paper-award-nominee", "title": "Mapping Networks: CVPR 2026 Best Paper Award Nominee", "summary": "Researchers introduced Mapping Networks, a novel approach that replaces high-dimensional weight spaces with compact latent vectors, achieving a 500x reduction in trainable parameters while maintaining comparable or better performance on vision and sequence tasks. The method, nominated for the CVPR 2026 Best Paper Award, addresses overfitting and training efficiency in large deep learning models.", "body_md": "# Computer Science > Computer Vision and Pattern Recognition\n\n[Submitted on 22 Feb 2026]\n\n# Title:Mapping Networks\n\n[View PDF](/pdf/2602.19134)\n\n[HTML (experimental)](https://arxiv.org/html/2602.19134v1)\n\nAbstract:The escalating parameter counts in modern deep learning models pose a fundamental challenge to efficient training and resolution of overfitting. We address this by introducing the \\emph{Mapping Networks} which replace the high dimensional weight space by a compact, trainable latent vector based on the hypothesis that the trained parameters of large networks reside on smooth, low-dimensional manifolds. Henceforth, the Mapping Theorem enforced by a dedicated Mapping Loss, shows the existence of a mapping from this latent space to the target weight space both theoretically and in practice. Mapping Networks significantly reduce overfitting and achieve comparable to better performance than target network across complex vision and sequence tasks, including Image Classification, Deepfake Detection etc, with $\\mathbf{99.5\\%}$, i.e., around $500\\times$ reduction in trainable parameters.\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/mapping-networks-cvpr-2026-best-paper-award-nominee", "canonical_source": "https://arxiv.org/abs/2602.19134", "published_at": "2026-06-26 07:08:53+00:00", "updated_at": "2026-06-26 07:35:10.098326+00:00", "lang": "en", "topics": ["machine-learning", "computer-vision", "neural-networks", "ai-research"], "entities": ["CVPR"], "alternates": {"html": "https://wpnews.pro/news/mapping-networks-cvpr-2026-best-paper-award-nominee", "markdown": "https://wpnews.pro/news/mapping-networks-cvpr-2026-best-paper-award-nominee.md", "text": "https://wpnews.pro/news/mapping-networks-cvpr-2026-best-paper-award-nominee.txt", "jsonld": "https://wpnews.pro/news/mapping-networks-cvpr-2026-best-paper-award-nominee.jsonld"}}