Title: Butterfly Effect: Bidirectional Control of Classification Performance by Small Additive Perturbation,
Abstract: This paper proposes a new algorithm for controlling classification results by
generating a small additive perturbation without changing the classifier
network. Our work is inspired by existing works generating adversarial
perturbation that worsens classification performance. In contrast to the
existing methods, our work aims to generate perturbations that can enhance
overall classification performance. To solve this performance enhancement
problem, we newly propose a perturbation generation network (PGN) influenced by
the adversarial learning strategy. In our problem, the information in a large
external dataset is summarized by a small additive perturbation, which helps to
improve the performance of the classifier trained with the target dataset. In
addition to this performance enhancement problem, we show that the proposed PGN
can be adopted to solve the classical adversarial problem without utilizing the
information on the target classifier. The mentioned characteristics of our
method are verified through extensive experiments on publicly available visual
datasets.