Mapping Networks: CVPR 2026 Best Paper Award Nominee
The paper titled Mapping Networks has been nominated for the CVPR 2026 Best Paper Award. The paper introduces a new concept called Mapping Networks, which aims to reduce overfitting in deep learning models by replacing high-dimensional weight space with a compact, trainable latent vector. The authors, Lord Sen and Shyamapada Mukherjee, demonstrate that Mapping Networks can achieve comparable or better performance than target networks with a significant reduction in trainable parameters.
- ▪The paper Mapping Networks has been nominated for the CVPR 2026 Best Paper Award.
- ▪Mapping Networks replace high-dimensional weight space with a compact, trainable latent vector.
- ▪The approach achieves a 99.5% reduction in trainable parameters and comparable or better performance than target networks.
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Record
| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2602.19134 |
| Publication time | Fri, 26 Jun 2026 07:08:53 +0000 |
| Retrieval time | 2026-06-26T07:37:30.198Z |
| Last seen | 2026-06-26T07:37:30.198Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | eh2TNbFSXawT |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.19134 (cs) [Submitted on 22 Feb 2026] Title:Mapping Networks Authors:Lord Sen, Shyamapada Mukherjee View a PDF of the paper titled Mapping Networks, by Lord Sen and 1 other authors View PDF HTML (experimental) Abstract: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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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