【论文阅读】Deep Generative Models on 3D Representations: A Survey(待续) 时穿之月 5 人赞同了该文章 Abstract 随着神经网络的兴起,变分自编码器 (variational autoencoder, VAE) 和生成对抗网络 (generative adversarial network, GAN) 等深度生成模型在二维图像合成方面取得了巨大进步。[1] 相比2D 数据,3D ...
whose purpose is to generate new graphs from a distribution similar to the observed graphs, has received increasing attention thanks to the recent advances of deep learning models. In this paper, we conduct a comprehensive review on the existing literature of deep graph generation...
Deep learning shows great potential in generation tasks thanks to deep latent representation. Generative models are classes of models that can generate observations randomly with respect to certain implied parameters. Recently, the diffusion Model becomes a raising class of generative models by virtue of...
[7] Belhassen Bayar and Matthew C Stamm. A deep learn-ing approach to universal image manipulation detectionusing a new convolutional layer. In ACM Workshop onInformation Hiding and Multimedia Security, 2016. [8] Belhassen Bayar and Matthew C Stamm. Constrainedconvolutional neural networks: A new...
deep fuzzy structures are summarized into two categories: (i) Standard DFS and (ii) Hybrid DFS. A model belongs to the first category when the blocks of fuzzy systems are stacked in series, in parallel, or hierarchically (see Fig.8a). Also, there are cases where the architecture of such...
DeepGMG (Deep generative model of graphs) 假设图的概率是所有可能节点置换的排列组合 公式表达: 表示节点顺序 ,用于得到图中所有节点和边的复杂联合概率 通过决策来确定是否需要添加节点,要添加哪个节点,是否添加边以及连接到新节点的节点 生成节点和边的决策过程取决于RecGNN更新的增长图的节点状态和图状态 ...
结构深网嵌入Structural Deep Network Embedding (SDNE) 深度递归网络的嵌入Deep Recursive Network Embedding (DRNE) 图生成网络 Graph Generative Networks 分子生成对抗网络 Molecular Generative Adversarial Networks (MolGAN) Deep Generative Models of Graphs (DGMG) ...
We providedDiffusion Model.pdf, the slide that serves as a vivid explanation for our article. Here, we not only thank for the articles cited in our survey, but also thank the "Tutorial on Denoising Diffusion-based Generative Modeling: Foundations and Applications" provided by NVIDIAtutorial. Besi...
recognition in mobile communication network based on deep learning,” in Signal and Information Processing, Networking and Computers, vol. 494 of Lecture Notes in Electrical Engineering, pp. 296–306, Springer, Singapore, 2019. [12] J. Schmidhuber, “Deep learning in neural networks: an ...
A Survey on Transferability of Adversarial Examples across Deep Neural Networksarxiv.org/abs/2310.17626 摘要 深度神经网络(DNN)的出现彻底改变了各个领域,使图像识别、自然语言处理和科学问题解决等复杂任务的解决成为可能。然而,这一进展也暴露了一个令人担忧的脆弱性:对抗性示例。这些精心制作的输入,人类无法...