Many components of data analysis in high energy physics and beyond require
morphing one dataset into another. This is commonly solved via reweighting, but
there are many advantages of preserving weights and shifting the data points
instead. Normalizing flows are machine learning models with impressive
precision on a variety of particle physics tasks. Naively, normalizing flows
cannot be used for morphing because they require knowledge of the probability
density of the starting dataset. In most cases in particle physics, we can
generate more examples, but we do not know densities explicitly. We propose a
protocol called flows for flows for training normalizing flows to morph one
dataset into another even if the underlying probability density of neither
dataset is known explicitly. This enables a morphing strategy trained with
maximum likelihood estimation, a setup that has been shown to be highly
effective in related tasks. We study variations on this protocol to explore how
far the data points are moved to statistically match the two datasets.
Furthermore, we show how to condition the learned flows on particular features
in order to create a morphing function for every value of the conditioning
feature. For illustration, we demonstrate flows for flows for toy examples as
well as a collider physics example involving dijet events

利用正则化流模型，在没有明确知道数据集概率密度的情况下，通过最大似然估计训练正则化流，实现从一个数据集到另一个数据集的变形策略，并研究数据点的移动程度以统计匹配两个数据集，同时以特定特征为条件来生成变形函数。

从一个数据集到另一个数据集的流动：最大似然估计的变形

Flows for Flows: Morphing one Dataset into another with Maximum  Likelihood Estimation

Multiple studies in the past have shown that there is a strong correlation
between human vocal characteristics and facial features. However, existing
approaches generate faces simply from voice, without exploring the set of
features that contribute to these observed correlations. A computational
methodology to explore this can be devised by rephrasing the question to: "how
much would a target face have to change in order to be perceived as the
originator of a source voice?" With this in perspective, we propose a framework
to morph a target face in response to a given voice in a way that facial
features are implicitly guided by learned voice-face correlation in this paper.
Our framework includes a guided autoencoder that converts one face to another,
controlled by a unique model-conditioning component called a gating controller
which modifies the reconstructed face based on input voice recordings. We
evaluate the framework on VoxCelab and VGGFace datasets through human subjects
and face retrieval. Various experiments demonstrate the effectiveness of our
proposed model.

本研究提出了一种基于先前学习到的语音 - 面部相关性的引导自编码器框架，通过将目标面孔形态学变换来匹配语音信息，显著提高了人脸检索和识别的准确性。

通过受控自编码器生成与声音相对应的人脸

Controlled AutoEncoders to Generate Faces from Voices

We present an algorithm that automatically establishes dense correspondences
between a large number of 3D faces. Starting from automatically detected sparse
correspondences on the outer boundary of 3D faces, the algorithm triangulates
existing correspondences and expands them iteratively by matching points of
distinctive surface curvature along the triangle edges. After exhausting
keypoint matches, further correspondences are established by generating evenly
distributed points within triangles by evolving level set geodesic curves from
the centroids of large triangles. A deformable model (K3DM) is constructed from
the dense corresponded faces and an algorithm is proposed for morphing the K3DM
to fit unseen faces. This algorithm iterates between rigid alignment of an
unseen face followed by regularized morphing of the deformable model. We have
extensively evaluated the proposed algorithms on synthetic data and real 3D
faces from the FRGCv2, Bosphorus, BU3DFE and UND Ear databases using
quantitative and qualitative benchmarks. Our algorithm achieved dense
correspondences with a mean localisation error of 1.28mm on synthetic faces and
detected $14$ anthropometric landmarks on unseen real faces from the FRGCv2
database with 3mm precision. Furthermore, our deformable model fitting
algorithm achieved 98.5% face recognition accuracy on the FRGCv2 and 98.6% on
Bosphorus database. Our dense model is also able to generalize to unseen
datasets.

该研究论文提出了一种算法，通过自动检测的稀疏对应点并通过匹配三角形边缘处的表面曲率的显著点迭代地三角化已有的对应点并扩展它们来自动建立大量三维人脸之间的密集对应，同时构建了一种可变形模型用于未见过人脸的变形拟合，经过广泛的数据评估表明，该算法在合成数据和实际人脸上都达到了较高的稳健性和精度。