# QuadEnhancer: Leveraging Quadratic Transformations to Enhance Deep Neural Networks Source: [QuadEnhancer: Leveraging Quadratic Transformations to Enhance Deep Neural Networks]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:51:17.805Z Authors: Qian Chen, Linxin Yang, Akang Wang, Xiaodong Luo, Yin Zhang Published: 2025-09-28T08:35:31.000Z ## Abstract The combination of linear transformations and non-linear activation functions forms the foundation of most modern deep neural networks, enabling them to approximate highly complex functions. This paper explores the introduction of quadratic transformations to further increase nonlinearity in neural networks, with the aim of enhancing the performance of existing architectures. To reduce parameter complexity and computational complexity, we propose a lightweight quadratic enhancer that uses low-rankness, weight sharing, and sparsification techniques. For a fixed architecture, the proposed approach introduces quadratic interactions between features at every layer, while only adding negligible amounts of additional model parameters and forward computations. We conduct a set of proof-of-concept experiments for the proposed method across three tasks: image classification, text classification, and fine-tuning large-language models. In all tasks, the proposed approach demonstrates clear and substantial performance gains. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()