A large number of works aim to alleviate the impact of noise due to an underlying conventional assumption of the negative role of noise. However, some existing works show that the assumption does not always hold. In this paper, we investigate how to benefit the classical models by random noise under the framework of Positive-incentive Noise (Pi-Noise). Since the ideal objective of Pi-Noise is intractable, we propose to optimize its variational bound instead, namely variational Pi-Noise (VPN). With the variational inference, a VPN generator implemented by neural networks is designed for enhancing base models and simplifying the inference of base models, without changing the architecture of base models. Benefiting from the independent design of base models and VPN generators, the VPN generator can work with most existing models. From the experiments, it is shown that the proposed VPN generator can improve the base models. It is appealing that the trained variational VPN generator prefers to blur the irrelevant ingredients in complicated images, which meets our expectations.

翻译：大量研究工作旨在减轻噪声的影响，其背后基于一个传统假设——噪声起负面作用。然而，现有部分研究表明该假设并非始终成立。本文研究如何在正激励噪声（Pi-Noise）框架下利用随机噪声提升经典模型性能。由于Pi-Noise的理想目标难以直接求解，我们提出优化其变分下界，即变分正激励噪声（VPN）。通过变分推理，我们设计了一个由神经网络实现的VPN生成器，用于增强基础模型并简化其推理过程，同时无需改变基础模型的结构。得益于基础模型与VPN生成器的独立设计，该生成器可与大多数现有模型协同工作。实验表明，所提出的VPN生成器能够有效提升基础模型性能。值得注意的是，训练完成的变分VPN生成器倾向于模糊复杂图像中的无关成分，这符合我们的预期。