Twisted Convolutional Networks (TCNs): Enhancing Feature Interactions for Non-Spatial Data Classification

Autor:	Lian, Junbo Jacob
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	Twisted Convolutional Networks (TCNs) are introduced as a novel neural network architecture designed to effectively process one-dimensional data with arbitrary feature order and minimal spatial relationships. Unlike traditional Convolutional Neural Networks (CNNs), which excel at handling structured two-dimensional data like images, TCNs reduce dependency on feature order by combining input features in innovative ways to create new representations. By explicitly enhancing feature interactions and employing diverse feature combinations, TCNs generate richer and more informative representations, making them especially effective for classification tasks on datasets with arbitrary feature arrangements. This paper details the TCN architecture and its feature combination strategy, providing a comprehensive comparison with traditional CNNs, DeepSets, Transformers, and Graph Neural Networks (GNNs). Extensive experiments on benchmark datasets demonstrate that TCNs achieve superior performance, particularly in classification scenarios involving one-dimensional data. Comment: The source code for the TCNs can be accessed at https://github.com/junbolian/Twisted-Convolutional-Networks
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.00238 Zobrazit plný text záznamu View this record from Arxiv