Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks NIPS 2015 摘要:本文提出一种 generative parametric model 能够产生高质量自然图像。我们的方法利用 Laplacian pyramid framework 的框架,从粗到细的方式,利用 CNN 的级联来产生图像。在金字塔的每一层,都用一个 GAN,我们的方法可以产生...
这是一些对于论文《Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks》的简单的读后总结,首先先奉上该文章的下载超链接:LAPGAN 这篇文章是由Courant Institute和Facebook AI Research的人员合作完成的,作者分别是Emily Denton、Soumith Chintala、Arthur Szlam和Rob Fergus。其是LAPGAN(Lap...
1 Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (DCGANs) [pdf] 2015 901 2 Explaining and Harnessing Adversarial Examples [pdf] 2014 536 3 Improved Techniques for Training GANs [pdf] 2016 436 4 Deep Generative Image Models using a Laplacian Pyramid of Adver...
从Eq. 3的定义中可以看出,ssm - supsubcls值越高,表示数据集中超类之间的相似性越高,深度生成模型很难重建图像中的细节来区分这个超类内的子类,因此从GM-augCls中获益的机会就越小。 cGAN-aug Classification Pipeline 一种提高分类性能的增强方法。图5提供了名为GMaugCls的管道的说明图。该图显示,以迭代的方式...
Wattenberg, “GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation,” IEEE Trans. Vis. Comput. Graph., vol. 25, no. 1, pp. 310–320, 2019.[211] A. Khan, A. Sohail, and A. Ali, “A New Channel Boosted Convolutional Neural Network using Transfer ...
Semantic inpainting, a technique for image processing using deep generative models, has been recently applied for this purpose, demonstrating its effectiveness ... Q Zheng,L Zeng,GE Karniadakis - 《Journal of Computational Physics》 被引量: 0发表: 2020年 An Improved GAN Semantic Image Inpainting ...
Generating a photorealistic image with intended human pose is a promising yet challenging research topic for many applications such as smart photo editing, movie making, virtual try-on, and fashion display. In this paper, we present a novel deep generative model to transfer an image of a person...
Generative image models are well studied and fall into two categories: parametric and non-parametric. 生成图像模型已得到深入研究,可分为两类:参数模型和非参数模型。 The non-parametric models often do matching from a database of existing images, often matching patches of images, and have been used...
本学期由人工智能学院主办的倒数第二场讲座是由清华大学的朱军老师带来的《Learning Deep Generative Models Reliably and Efficiently》讲座,朱老师主要从生成模型的简介、训练和优化深度生成模型的方法以及一些团队工作展开。 朱老师首先回顾了简单...
Fig. 1. An overview of different generative models. GAN: 在数据合成方面具有强大性能,故在数据生成方面非常受欢迎。它通常由两个独立网络组成:生成器(generator) G(\cdot) 将从先验分布 z ∼ p_z 中采样获得隐编码(latent code,或称潜在编码)以作为输入,然后创建数据;鉴别器(discriminator) D(\cdot) 旨...