← 研踪 PaperTrailCVF · CVPR 2026:cvf:20e98f0e75aaf58ff98f93ecda7f1734 · 记录 #1
Mapping Networks
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 02:41
摘要
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 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 99.5%, i.e., around 500x reduction in trainable parameters.
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记录 #1 · 2026 · 最新观测版本
Mapping Networks
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 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 99.5%, i.e., around 500x reduction in trainable parameters.
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