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.

本文研究在正奖励噪声框架下，如何通过随机噪声使经典模型受益，并提出了优化其变分下界的变分 Pi 噪声（VPN）提高基本模型的生成器，其独立设计可以与大多数现有模型一起使用，并且性能可以得到改善，VPN 生成器可以针对复杂的图像去除不必要的元素，满足我们的期望。