  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.
