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...
Generative Adversarial Network (GAN) Energy-based Models Score matching Diffusion Models(扩散模型) Consistency Models Flow Matching book Probabilistic Machine Learning: Advanced Topics by Kevin Patrick Murphy. MIT Press, 2023. 链接 概率模型综述类,大而全、理论完备、自成体系、适合参考 (强烈推荐,适合学习...
et al. Learning interpretable representations of entanglement in quantum optics experiments using deep generative models. Nat Mach Intell 4, 544–554 (2022). https://doi.org/10.1038/s42256-022-00493-5 Download citation Received04 October 2021 Accepted01 May 2022 Published16 June 2022 Issue Date...
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...
CS224w Lecture 10: Deep Generative Models for Graphs 上图为CS224W第十讲的内容框架,如下链接为第十讲的课程讲义 1 Problem of Graph Generation 我们可先带着如下两个问题,开始本章图生成模型的学习。 1)生成模型应该怎么设计,我们才能用它来生成图? 2)如何评价图的生成模型?什么样的模型才是好的生成模型?
Recently, the so-called deep generative models (DGMs) that were mainly exploited for image generation [24], have been applied also for time-series and ECG signals generation. These ML-based models are able to generate a large quantity of synthetic data, with greater variability, starting from ...
Code:semantic_image_inpainting 2|0介绍 语义修复(Semantic−inpaintingSemantic−inpainting):是指根据图像的语义信息来推断图像中任意大的缺失区域内容。 典型图像修复方法包括:基于局部信息和非局部信息来修复图像。现在大多数的修复方法是基于单个图像修复(利用图片局部信息)而设计的,利用输入图像提供的信息,并利用ima...
To address these limitations, we present SpatialScope, a unified approach integrating scRNA-seq reference data and ST data using deep generative models. With innovation in model and algorithm designs, SpatialScope not only enhances seq-based ST data to achieve single-cell resolution, but also ...
Given a trained generative model, we search for the closest encoding of the corrupted image in the latent image manifold using our context and prior losses. This encoding is then passed through the generative model to infer the missing content. In our method, inference is possible irrespective ...