Generator Born from Classifier
Runpeng Yu Xinchao Wang
In this paper, we make a bold attempt toward an ambitious task: given a
pre-trained classifier, we aim to reconstruct an image generator, without
relying on any data samples. From a black-box perspective, this challenge seems
intractable, since it inevitably involves identifying the inverse function for
a classifier, which is, by nature, an information extraction process. As such,
we resort to leveraging the knowledge encapsulated within the parameters of the
neural network. Grounded on the theory of Maximum-Margin Bias of gradient
descent, we propose a novel learning paradigm, in which the generator is
trained to ensure that the convergence conditions of the network parameters are
satisfied over the generated distribution of the samples. Empirical validation
from various image generation tasks substantiates the efficacy of our strategy.