Human Annotations Improve GAN Performances

TL;DR

This paper introduces a novel approach that uses human-annotated attributes to enhance GAN image quality, demonstrating improved performance through attribute integration and analysis.

Contribution

It presents a new method leveraging human annotations of image attributes to improve GAN training and output quality, including a dataset and attribute-based discrimination.

Findings

Attributes effectively distinguish real and fake images.

Attribute integration improves GAN image quality.

Deep models can predict annotated attributes accurately.

Abstract

Generative Adversarial Networks (GANs) have shown great success in many applications. In this work, we present a novel method that leverages human annotations to improve the quality of generated images. Unlike previous paradigms that directly ask annotators to distinguish between real and fake data in a straightforward way, we propose and annotate a set of carefully designed attributes that encode important image information at various levels, to understand the differences between fake and real images. Specifically, we have collected an annotated dataset that contains 600 fake images and 400 real images. These images are evaluated by 10 workers from the Amazon Mechanical Turk (AMT) based on eight carefully defined attributes. Statistical analyses have revealed different distributions of the proposed attributes between real and fake images. These attributes are shown to be useful in discriminating fake images from real ones, and deep neural networks are developed to automatically predict the attributes. We further utilize the information by integrating the attributes into GANs to generate better images. Experimental results evaluated by multiple metrics show performance improvement of the proposed model.