Discriminator Feature-based Inference by Recycling the Discriminator of GANs

TL;DR

This paper introduces a novel inference algorithm that leverages GAN discriminator features to accurately map real data to latent space, enhancing semantic accuracy and enabling advanced conditional image generation.

Contribution

The paper proposes a discriminator feature-based inference method that improves latent space mapping accuracy with minimal training, facilitating better conditional image generation.

Findings

Outperforms existing inference methods in semantic accuracy

Enables effective spatially conditioned image generation

Requires minimal additional training overhead

Abstract

Generative adversarial networks (GANs)successfully generate high quality data by learning amapping from a latent vector to the data. Various studies assert that the latent space of a GAN is semanticallymeaningful and can be utilized for advanced data analysis and manipulation. To analyze the real data in thelatent space of a GAN, it is necessary to build an inference mapping from the data to the latent vector. Thispaper proposes an effective algorithm to accurately infer the latent vector by utilizing GAN discriminator features. Our primary goal is to increase inference mappingaccuracy with minimal training overhead. Furthermore,using the proposed algorithm, we suggest a conditionalimage generation algorithm, namely a spatially conditioned GAN. Extensive evaluations confirmed that theproposed inference algorithm achieved more semantically accurate inference mapping than existing methodsand can be successfully applied to advanced conditionalimage generation tasks.