To enable such memory-intensive end-to-end finetuning, we propose a novel two-level invertible design to transform both (1) the multi-step sampling process and (2) the noise estimation U-Net in each step into invertible networks. As a result, most intermediate features are cleared during ...
题目:Glow: Generative Flowwith Invertible 1×1 Convolutions 摘要:Glow:具有可逆1×1卷积的生成流 摘要 : 基于流的生成模型(Dinh等人,2014)在概念上很有吸引力,这是因为精确的对数似然性的可预测性,精确的潜在变量推断的可预测性以及训练和综合的可并行性。在本文中,我们提出了Glow,一种使用可逆1×... ...
A SLICES string always begins with symbols of atoms in the unit cell (Fig.1b), encoding the chemical composition of the corresponding crystal structure. In SLICES, edges are represented explicitly in the form\({u\; v\; x\; y\; z}\)(Fig.1b), where\({u\; v}\)are node indices a...
flow在于density evaluation是tractable的，而GAN/VAE都没办法求得数据点的probability density 这篇综述可以...
1.足够复杂，容得下多个模式，比如增强学习中的图像和评分函数； 2.足够简单，能采样，能估计密度，能...
个人浅见，flow(没看过最新进展，仅根据原始的模型来理解)是要求寻求一个分布使得训练数据落在分布的高...
In contrast, the CNN model yields a smoother prediction owing to the convolution operation inherent in its architecture. However, the CNN model wrongly predicts the presence of void fraction in regions characterized by high conductivity, as visible in the results of Sample 1. We believe that this...
2D convolution can be implemented using matrix multiplication involving a Toeplitz matrix [28]. Toeplitz matrix is obtained from the set of kernels of the 2D convolutional filters. Thus, transformation 𝑇𝐸TE and its inverse (𝑇𝐸)−1(TE)−1 can be explained with the same equations ...
2D convolution can be implemented using matrix multiplication involving a Toeplitz matrix [28]. Toeplitz matrix is obtained from the set of kernels of the 2D convolutional filters. Thus, transformation 𝑇𝐸TE and its inverse (𝑇𝐸)−1(TE)−1 can be explained with the same equations ...