In this paper, we propose a multi-attention generative adversarial networks (MAGAN) for text-to-image generation. We use self-attention mechanism to improve the overall quality of images, so that the target image with a certain structure can also be generated well. We use multi-head attention...
Finding the right balance between the generator and discriminator networks is crucial for successful training. Evaluation Metrics Evaluating the performance of GANs is a challenging task. Traditional metrics such as accuracy or perplexity do not capture the quality of the generated samples. Developing ...
context=cs代码地址: https://github.com/MinfengZhu/DM-GAN @[TOC](Text to image论文精读 DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis 用于文本图像合成的动态记忆生成对抗网络) 这篇文章提出了动态记忆生成对抗网络(DM-GAN)来生成高质量的图像。该方法可以在初始图像生...
In this paper, we propose an adversarial process forive text summarization, in which we simultaneously train a generative model G and a discriminative model D. In particular, we build the generator G as an agent of reinforcement learning, which takes the raw text as input and predicts theive...
^Alec Radford & Luke Metz, 2016. UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS ^abcHuiwen Chang et al., 2018. PairedCycleGAN: Asymmetric Style Transfer for Applying and Removing Makeup ^abChung-Yi Weng et al., 2018. Photo Wake-Up: 3D Character Animati...
曾经就有人问过,GANs能否被使用在文字生成领域,在Generative Adversarial Networks for Text一文(可见参考文献部分)中GANs的作者Ian Goodfellow就层明确给出了回答。让人心寒的是,作者给出的答案是否认的成分居多,并且还提出了一些理由: 从文中的一些表达例如:“there is no way...”和“no one really knows......
[6] Santiago Pascual, Antonio Bonafonte, and Joan Serra, “Segan: Speech enhancement generative adversarial network,”Proc. Interspeech 2017, pp. 3642–3646, 2017. [7] Deepak Baby and Sarah Verhulst, “Sergan: Speech enhancement using relativistic generative adversarial networks with gradient penalty...
In this paper, we propose a prototype design for manifold refinement to fine grained text-to-image generation by using Attentional Generative Adversarial Network (AttnGAN) We concentrate on creating realistic images from text descriptions. We have used a
人工智能论文-Generative Adversarial Networks (GANs) 热度: 相关推荐 李宏毅 Hung-yiLee Generator “Girlwith redhair” Generator −0.3 0.1 ⋮ 0.9 randomvector ThreeCategoriesofGAN 1.TypicalGAN image 2.ConditionalGAN Generator text imagepaireddata blueeyes, redhair, shorthair 3.UnsupervisedConditionalGAN...
resolutionimages.YouwillalsolearnhowtoimplementconditionalGANsthatgiveyoutheabilitytocontrolcharacteristicsofGANoutputs.YouwillbuildonyourknowledgefurtherbyexploringanewtrainingmethodologyforprogressivegrowingofGANs.Movingon,you'llgaininsightsintostate-of-the-artmodelsinimagesynthesis,speechenhancement,andnaturallanguage...