基于能量模型的伪非线性数据增强，可精准控制数据变换方向
Pseudo-Nonlinear Data Augmentation: A Constrained Energy Minimization Viewpoint
- 用能量模型构建几何感知的潜在空间，直观表达数据结构
- 在多个下游任务中表现媲美主流方法，且控制粒度更细
- 适合需要可控数据增强的科研与工业场景
我们提出一种适用于通用数据模态的简单而新颖的数据增强方法，基于能量模型和信息几何原理。不同于依赖生成模型学习潜在表示的现有方法，本框架能够直观构建反映数据自身结构的几何感知潜在空间，支持高效且明确的编码与解码过程。我们进一步阐述了如何设计后续用于控制数据增强的潜在空间，并展示了所提算法的设计思路。实证结果表明，该数据增强方法在下游任务中性能与其它基线相当，同时具备现有文献中缺乏的细粒度可控性。
原文摘要 · Abstract (English)
We propose a simple yet novel data augmentation method for general data modalities based on energy-based modeling and principles from information geometry. Unlike most existing learning-based data augmentation methods, which rely on learning latent representations with generative models, our proposed framework enables an intuitive construction of a geometrically aware latent space that represents the structure of the data itself, supporting efficient and explicit encoding and decoding procedures. We then present and discuss how to design latent spaces that will subsequently control the augmentation with the proposed algorithm. Empirical results demonstrate that our data augmentation method achieves competitive performance in downstream tasks compared to other baselines, while offering fine-grained controllability that is lacking in the existing literature.
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