[2602.19134] Mapping Networks
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2602.19134 (cs)
[Submitted on 22 Feb 2026]
Title:Mapping Networks
Authors:Lord Sen, Shyamapada Mukherjee<br>View a PDF of the paper titled Mapping Networks, by Lord Sen and 1 other authors
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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. 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.
Comments:<br>10 pages
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as:<br>arXiv:2602.19134 [cs.CV]
(or<br>arXiv:2602.19134v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2602.19134
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arXiv-issued DOI via DataCite
Submission history<br>From: Lord Sen [view email]<br>[v1]<br>Sun, 22 Feb 2026 11:33:23 UTC (6,419 KB)
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