Discrete transforms, such as the discrete Fourier transform, are widely used in machine learning to improve model performance by extracting meaningful features. However, with numerous transforms available, selecting an appropriate one often depends on understanding the dataset's properties, making the approach less effective when such knowledge is unavailable. In this work, we propose General Transform (GT), an adaptive transform-based representation designed for machine learning applications. Unlike conventional transforms, GT learns data-driven mapping tailored to the dataset and task of interest. Here, we demonstrate that models incorporating GT outperform conventional transform-based approaches across computer vision and natural language processing tasks, highlighting its effectiveness in diverse learning scenarios.

本研究解决了在缺乏数据集特性知识的情况下，选择合适离散变换的问题。提出的“一般变换”（GT）是一种自适应的基于变换的表示方法，可以根据数据集和特定任务学习数据驱动的映射。研究表明，加入GT的模型在计算机视觉和自然语言处理任务中的表现优于传统变换方法，显示出其在多种学习场景中的有效性。

一般变换：增强表示的自适应变换统一框架