Exploring Generative Adversarial Networks for Text-to-Image Generation with Evolution Strategies

07/06/2022
by   Victor Costa, et al.
10

In the context of generative models, text-to-image generation achieved impressive results in recent years. Models using different approaches were proposed and trained in huge datasets of pairs of texts and images. However, some methods rely on pre-trained models such as Generative Adversarial Networks, searching through the latent space of the generative model by using a gradient-based approach to update the latent vector, relying on loss functions such as the cosine similarity. In this work, we follow a different direction by proposing the use of Covariance Matrix Adaptation Evolution Strategy to explore the latent space of Generative Adversarial Networks. We compare this approach to the one using Adam and a hybrid strategy. We design an experimental study to compare the three approaches using different text inputs for image generation by adapting an evaluation method based on the projection of the resulting samples into a two-dimensional grid to inspect the diversity of the distributions. The results evidence that the evolutionary method achieves more diversity in the generation of samples, exploring different regions of the resulting grids. Besides, we show that the hybrid method combines the explored areas of the gradient-based and evolutionary approaches, leveraging the quality of the results.

READ FULL TEXT

page 1

page 2

page 4

page 5

page 6

page 7

research
09/28/2020

EvolGAN: Evolutionary Generative Adversarial Networks

We propose to use a quality estimator and evolutionary methods to search...
research
05/24/2019

Generative Latent Flow: A Framework for Non-adversarial Image Generation

Generative Adversarial Networks (GANs) have been shown to outperform non...
research
06/17/2019

Inspirational Adversarial Image Generation

The task of image generation started to receive some attention from arti...
research
11/07/2022

Few-shot Image Generation with Diffusion Models

Denoising diffusion probabilistic models (DDPMs) have been proven capabl...
research
12/16/2020

Latent Space Conditioning on Generative Adversarial Networks

Generative adversarial networks are the state of the art approach toward...
research
08/19/2020

Improving Text to Image Generation using Mode-seeking Function

Generative Adversarial Networks (GANs) have long been used to understand...
research
04/03/2018

DeSIGN: Design Inspiration from Generative Networks

Can an algorithm create original and compelling fashion designs to serve...

Please sign up or login with your details

Forgot password? Click here to reset