Providing Opportunities for young people to observe, participate and influence decisions that impact their future
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In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images.
SOTA for DFO on Oxford 102 Flowers
Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications.
In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation.
SOTA for DFO on CUB
Interest in derivative-free optimization (DFO) and "evolutionary strategies" (ES) has recently surged in the Reinforcement Learning (RL) community, with growing evidence that they can match state of the art methods for policy optimization problems in Robotics.
Our experiments show that through the use of the object pathway we can control object locations within images and can model complex scenes with multiple objects at various locations.
SOTA for DFO on MS-COCO
This block enables MC-GAN to generate a realistic object image with the desired background by controlling the amount of the background information from the given base image using the foreground information from the text attributes.
Generating an image from a given text description has two goals: visual realism and semantic consistency.