映射网络
Mapping Networks
用可训练的紧凑潜向量替代高维权重空间，把训练参数量压缩约500倍，为视觉大模型（分类、Deepfake检测等）提供了一条以低维流形假设驱动的参数高效训练与抗过拟合路径，可直接迁移到其他网络的训练流程中。
原文依据 replace the high dimensional weight space by a compact, trainable latent vector
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论现代深度学习模型参数量的不断攀升，对高效训练和过拟合的解决构成了根本性挑战。我们基于「大网络训练后的参数位于光滑的低维流形上」这一假设，提出映射网络（Mapping Networks），用一个紧凑的可训练潜向量替代高维权重空间。由此，由专门的映射损失所施加的映射定理，在理论上和实践上都证明了从该潜空间到目标权重空间的映射的存在性。映射网络显著降低了过拟合，并在包括图像分类、Deepfake 检测等在内的复杂视觉与序列任务上取得与目标网络相当甚至更好的性能，同时可训练参数减少 99.5%，即约 500 倍。
英文原文摘要 865 字符
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.