General Transform: A Unified Framework for Adaptive Transform to Enhance Representations
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866913826367078400 |
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| author | Budiutama, Gekko Daimon, Shunsuke Nishi, Hirofumi Matsushita, Yu-ichiro |
| author_facet | Budiutama, Gekko Daimon, Shunsuke Nishi, Hirofumi Matsushita, Yu-ichiro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04969 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | General Transform: A Unified Framework for Adaptive Transform to Enhance Representations Budiutama, Gekko Daimon, Shunsuke Nishi, Hirofumi Matsushita, Yu-ichiro Machine Learning Computation and Language Computer Vision and Pattern Recognition 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. |
| title | General Transform: A Unified Framework for Adaptive Transform to Enhance Representations |
| topic | Machine Learning Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.04969 |