DeepAI AI Chat
Log In Sign Up

GIQA: Generated Image Quality Assessment

03/19/2020
by   Shuyang Gu, et al.
Microsoft
USTC
0

Generative adversarial networks (GANs) have achieved impressive results today, but not all generated images are perfect. A number of quantitative criteria have recently emerged for generative model, but none of them are designed for a single generated image. In this paper, we propose a new research topic, Generated Image Quality Assessment (GIQA), which quantitatively evaluates the quality of each generated image. We introduce three GIQA algorithms from two perspectives: learning-based and data-based. We evaluate a number of images generated by various recent GAN models on different datasets and demonstrate that they are consistent with human assessments. Furthermore, GIQA is available to many applications, like separately evaluating the realism and diversity of generative models, and enabling online hard negative mining (OHEM) in the training of GANs to improve the results.

READ FULL TEXT

page 2

page 6

page 11

11/08/2019

Quality Aware Generative Adversarial Networks

Generative Adversarial Networks (GANs) have become a very popular tool f...
01/28/2022

Generalized Visual Quality Assessment of GAN-Generated Face Images

Recent years have witnessed the dramatically increased interest in face ...
07/25/2018

How good is my GAN?

Generative adversarial networks (GANs) are one of the most popular metho...
12/08/2021

Assessing a Single Image in Reference-Guided Image Synthesis

Assessing the performance of Generative Adversarial Networks (GANs) has ...
10/26/2022

Towards the Detection of Diffusion Model Deepfakes

Diffusion models (DMs) have recently emerged as a promising method in im...
09/06/2022

Studying Bias in GANs through the Lens of Race

In this work, we study how the performance and evaluation of generative ...