Normalizing flow介绍: 生成模型 generative model 的类型 Generative models with tractable explicit density 值的是p(x)可以直接计算出来,包括了大部分auto regressive model (NADE, MADE, PixelRNN/CNN),还包括flow-based model(通过变量替换,用p(z)通过变换得到p(x),即x=T(z)。 Flow-based ...
为了拟合一个概率模型,我们要拟合一个flow-based model p_{x}(\mathbf{x};\theta)去近似目标分布p_{x}^{*}(\mathbf{x}),\theta代表(\phi,\psi),\phi和\psi分别是T与p(u)的参数,可以通过最小化KL散度和最大似然估计做到 2.3.1 正向KL散度与最大似然估计 \begin{eqnarray}{} \mathcal{L}(\bold...
具体来说，标准化流利用一系列简单变换的复合，如Flow-based model，这些变换的逆和微分要求满足可乘性。神经网络中的双射函数，如[公式]，按需实现单向或双向映射，构建出流模型。核心是寻找一个可逆且雅可比行列式易于计算的变换，通常通过优化为三角矩阵来简化计算。与VAE和GAN相比，标准化流的优势在于...
Third, the model should not be too large (achieving a small model size). However, existing data-driven methods cannot simultaneously optimize the three goals. To address the limitations, we propose a novel cardinality estimator FACE , which leverages the normalizing flow-based model to learn...
VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation Manoj KumarMohammad BabaeizadehDumitru Erhan...Durk Kingma Mar 2019 Generative models that can model and predict sequences of future events can, in principle, learn to capture complex real-world phenomena, such as physical inte...
Normalizing flow is a generative modeling approach with efficient sampling. However, Flow-based models suffer two issues: 1) If the target distribution is manifold, due to the unmatch between the dimensions of the latent target distribution and the data distribution, flow-based models might perform...
For engineering considerations, the time consumption of depth estimation and normalized flow modules is taken into account in both the CNN-based model (D4LCN) and our transformer-based model (DVT and NF-DVT), resulting in a much slower inference speed than a model without additional input like...
This is project page for the paper "RG-Flow: a hierarchical and explainable flow model based on renormalization group and sparse prior". Paper link:https://arxiv.org/abs/2010.00029 computer-visiongenerative-modelrepresentation-learningsparse-codinghierarchical-modelsrenormalization-groupnormalizing-flow ...
Aiming at more tractable and expressive variational families, in this work we extend the flow-based generative model to CF for modeling implicit feedbacks. We present the Collaborative Autoregressive Flows (CAF) for the recommender system, transforming a simple initial density into more complex ones ...
站在统计机器学习的角度上宏观来看，flow-based model具有以下的标签：likelihood-based（概率密度模型），...