2,547 research outputs found
Adversarial nets with perceptual losses for text-to-image synthesis
Recent approaches in generative adversarial networks (GANs) can automatically
synthesize realistic images from descriptive text. Despite the overall fair
quality, the generated images often expose visible flaws that lack structural
definition for an object of interest. In this paper, we aim to extend state of
the art for GAN-based text-to-image synthesis by improving perceptual quality
of generated images. Differentiated from previous work, our synthetic image
generator optimizes on perceptual loss functions that measure pixel, feature
activation, and texture differences against a natural image. We present
visually more compelling synthetic images of birds and flowers generated from
text descriptions in comparison to some of the most prominent existing work
Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation
Image-to-image translation has been made much progress with embracing
Generative Adversarial Networks (GANs). However, it's still very challenging
for translation tasks that require high quality, especially at high-resolution
and photorealism. In this paper, we present Discriminative Region Proposal
Adversarial Networks (DRPAN) for high-quality image-to-image translation. We
decompose the procedure of image-to-image translation task into three iterated
steps, first is to generate an image with global structure but some local
artifacts (via GAN), second is using our DRPnet to propose the most fake region
from the generated image, and third is to implement "image inpainting" on the
most fake region for more realistic result through a reviser, so that the
system (DRPAN) can be gradually optimized to synthesize images with more
attention on the most artifact local part. Experiments on a variety of
image-to-image translation tasks and datasets validate that our method
outperforms state-of-the-arts for producing high-quality translation results in
terms of both human perceptual studies and automatic quantitative measures.Comment: ECCV 201
- …