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In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images.
Ranked #1 on Text-to-Image Generation on Oxford 102 Flowers
Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications.
Ranked #2 on Text-to-Image Generation on Oxford 102 Flowers
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.
Ranked #3 on Text-to-Image Generation on COCO (SOA-C metric)
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.
To address these challenges we introduce a new model that explicitly models individual objects within an image and a new evaluation metric called Semantic Object Accuracy (SOA) that specifically evaluates images given an image caption.
Ranked #1 on Text-to-Image Generation on COCO
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.
Ranked #2 on Text-to-Image Generation on COCO
In this paper, we propose a novel controllable text-to-image generative adversarial network (ControlGAN), which can effectively synthesise high-quality images and also control parts of the image generation according to natural language descriptions.
Ranked #2 on Text-to-Image Generation on CUB
If the initial image is not well initialized, the following processes can hardly refine the image to a satisfactory quality.
Ranked #1 on Text-to-Image Generation on CUB