DRIFT：通过信息特征变换进行数据降维——泛化在深度学习开始之前就已发生
DRIFT: Data Reduction via Informative Feature Transformation- Generalization Begins Before Deep Learning starts
AI总结：
本文提出DRIFT预处理技术，受物理振动分析启发提取数据共振模式，将图像投影到低维物理基上，以极少的输入维度实现具竞争力的准确率，显著提升了训练稳定性与泛化鲁棒性。
摘要：
现代深度学习架构擅长优化，但这仅在数据进入网络之后。真正的瓶颈在于准备合适的输入：数据需最小化、显著，并以反映数据本质模式的方式构建。我们提出了DRIFT（Data Reduction via Informative Feature Transformation，通过信息特征变换进行数据降维），这是一种受物理系统振动分析启发的新型预处理技术，旨在训练前识别并提取输入数据中最具共振性的模式。不同于在信号和噪声中共同学习的传统模型，DRIFT模拟物理感知，强调信息特征并丢弃无关元素。其结果是一种更紧凑且可解释的表示，能增强训练稳定性和泛化性能。在DRIFT中，图像被投影到由平板空间振动模态形状构成的低维基上，提供了具有物理基础的特征集。这使得神经网络能够以极少的输入维度运行（在MNIST上约50个特征，在CIFAR100上不到100个），同时实现具竞争力的分类准确率。在MNIST和CIFAR100上的大量实验表明，DRIFT在训练稳定性、抵抗过拟合及泛化鲁棒性方面优于基于标准像素的模型和PCA。值得注意的是，DRIFT对批量大小、网络架构和图像分辨率的变化表现出极小的敏感性，进一步确立了其作为鲁棒且高效数据表示策略的地位。这项工作将焦点从架构工程转移到输入整理，并强调了物理驱动的数据变换在提升深度学习性能方面的力量。
英文摘要：
Modern deep learning architectures excel at optimization, but only after the data has entered the network. The true bottleneck lies in preparing the right input: minimal, salient, and structured in a way that reflects the essential patterns of the data. We propose DRIFT (Data Reduction via Informative Feature Transformation), a novel preprocessing technique inspired by vibrational analysis in physical systems, to identify and extract the most resonant modes of input data prior to training. Unlike traditional models that attempt to learn amidst both signal and noise, DRIFT mimics physics perception by emphasizing informative features while discarding irrelevant elements. The result is a more compact and interpretable representation that enhances training stability and generalization performance. In DRIFT, images are projected onto a low-dimensional basis formed by spatial vibration mode shapes of plates, offering a physically grounded feature set. This enables neural networks to operate with drastically fewer input dimensions (~ 50 features on MNIST and less than 100 on CIFAR100) while achieving competitive classification accuracy. Extensive experiments across MNIST and CIFAR100 demonstrate DRIFT's superiority over standard pixel-based models and PCA in terms of training stability, resistance to overfitting, and generalization robustness. Notably, DRIFT displays minimal sensitivity to changes in batch size, network architecture, and image resolution, further establishing it as a resilient and efficient data representation strategy. This work shifts the focus from architecture engineering to input curation and underscores the power of physics-driven data transformations in advancing deep learning performance.
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