Variational Autoencoders 在变分自编码器(VAE)的默认公式中,我们直接最大化ELBO。这种方法是"变分"的,因为它在一个由参数{\bm{\phi}}参数化的后验分布的潜在族群中寻找最佳的q_{\bm{\phi}}({\bm{z}}|{\bm{x}})。它被称为自编码器,因为它类似于传统的自编码模型,输入数据会经过一个瓶颈表示步骤后进...
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Variational Diffusion Models 变分扩散模型(Variational Diffusion Models, VDM)可以被简单理解为具有三个关键限制的马尔可夫层次变分自编码器(Markovian Hierarchical Variational Autoencoder, MHVAE): 潜在维度严格等于数据维度。 每个时间步的潜在编码器的结构不是学习得到的;它被预定义为线性高斯模型。换句话说,它是一个...
Variational Autoencoders (VAEs). In a nutshell, a VAE is an autoencoder whose encodings distribution is regularised during the training in order to ensure that its latent space has good properties allowing us to generate some new data. Moreover, the term “variational” comes from the close...
与此相对,像归一化流(normalizing flows)、变分自编码器 (Variational Autoencoders, VAEs) 这类的概率生成模型 (probabilistic generative models) 则旨在学习覆盖数据的分布 P r(x) (见图 14.2)。训练结束后,可以从该分布中抽取(生成)样本。然而,由于 VAE 的特性,遗憾的是无法精确计算新样本 x...
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标准化流(Normalizing Flows),变分自编码器(Variational Autoencoders,VAEs)和扩散模型(Diffusion Models)(第 16 至 18 章)属于概率生成模型。除了生成新的示例外,它们还为每个数据点 x 分配一个概率 Pr(x|ϕ)。这个概率取决于模型参数 ϕ,在训练过程中,我们的目标是最大化观测数据 {x_i} 的概率,...
A variation auto-encoder (VAE)40 network architecture was chosen for its modular nature. VAEs consist of two main subnetwork components which include an encoder and a generator. In brief, the encoder portion of the network is tasked with learning the important imaging information from the DXA ...
2024-04-18 Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery Yona Falinie A. Gaus et.al. 2404.12285 null 2024-04-18 Enhancing Embedding Performance through Large Language Model-based Text Enrichment and Rewriting Nicholas Harris ...
与此相对,像归一化流(normalizing flows)、变分自编码器 (Variational Autoencoders, VAEs) 这类的概率生成模型 (probabilistic generative models) 则旨在学习覆盖数据的分布 P r(x) (见图 14.2)。训练结束后,可以从该分布中抽取(生成)样本。然而,由于 VAE 的特性,遗憾的是无法精确计算新样本 x∗ 的...