texttoimagegan

由 M Tao 著作 · 2022 · 被引用 195 次 — Synthesizing high-quality realistic images from text de- scriptions is a challenging task. Existing text-to-image Gen-.

由 S Reed 著作 · 被引用 3813 次 — Our model can in many cases generate visually-plausible 64×64 images con- ditioned on text, and is also distinct in that our entire model is a GAN, rather only ...

We introduce GigaGAN, a new GAN architecture that far exceeds this limit, demonstrating GANs as a viable option for text-to-image synthesis. GigaGAN offers ...

Text-to-image creation with generative adversarial networks (GAN) is a deep learning model that can produce images from text descriptions.

由 W Liao 著作 · 2022 · 被引用 109 次 — Text-to-image synthesis (T2I) aims to generate photo- realistic images which are semantically consistent with the text descriptions.

由 F Quan 著作 · 2022 · 被引用 8 次 — In this paper, we propose an ARRPNGAN model for text-to-image synthesis. In the generator of our model, a multilayer attention regularization structure is ...

Text-to-Image Generation is a task in computer vision and natural language processing where the goal is to generate an image that corresponds to a given ...

由 H Ku 著作 · 2023 · 被引用 15 次 — Text-to-image synthesis refers to the process of producing images from textual descriptions, which presents a formidable challenge as it necessitates the ...

This is a PyTorch-based implementation of the Generative Adversarial Text-to-Image Synthesis paper, utilizing a GAN architecture inspired by DCGAN with text ...

由 M Kang 著作 · 2023 · 被引用 233 次 — We introduce GigaGAN, a new GAN architecture that far exceeds this limit, demonstrating GANs as a viable option for text-to-image synthesis.