General Transform: A Unified Framework for Adaptive Transform to Enhance Representations

Discrete transforms, such as the discrete Fourier transform, a re widely used in machine learning to improve model performance by extracting mea ningful features. However, with numerous transforms available, selectin g an appropriate one often depends on understanding the dataset's proper ties, making the approach less effective when such knowledge is unavailable. In th is work, we propose General Transform (GT), an adaptive transform-ba sed representation designed for machine learning applications. Unlike convent ional transforms, GT learns data-driven mapping tailored to the datase t and task of interest. Here, we demonstrate that models incorporating GT o utperform conventional transform-based approaches across computer v ision and natural language processing tasks, highlighting its effectiveness in diverse learning scenarios. Keywords: machine learning, deep learning, feature extraction 1. Introduction Deep neural networks have consistently pushed the boundaries o f performance on tasks in computer vision, natural language processing, a nd beyond. Corresponding author Email address: bgekko@quemix.com