Diffusion models have gained traction as powerful algorithms for synthesizing
high-quality images. Central to these algorithms is the diffusion process,
which maps data to noise according to equations inspired by thermodynamics and
can significantly impact performance. A widely held assumption is that the ELBO
objective of a diffusion model is invariant to the noise process (Kingma et
al.,2021). In this work, we dispel this assumption -- we propose multivariate
learned adaptive noise (MuLAN), a learned diffusion process that applies
Gaussian noise at different rates across an image. Our method consists of three
components -- a multivariate noise schedule, instance-conditional diffusion,
and auxiliary variables -- which ensure that the learning objective is no
longer invariant to the choice of the noise schedule as in previous works. Our
work is grounded in Bayesian inference and casts the learned diffusion process
as an approximate variational posterior that yields a tighter lower bound on
marginal likelihood. Empirically, MuLAN sets a new state-of-the-art in density
estimation on CIFAR-10 and ImageNet compared to classical diffusion. Code is
available at this https URL

通过引入多元学习自适应噪声 (MuLAN)，这篇研究提出了一种新的扩散过程，它在图像中以不同的速率应用高斯噪声，以实现贝叶斯推断，获得更紧的边际似然下界，并在 CIFAR-10 和 ImageNet 数据集上实现了密度估计的最新技术。

学习自适应噪声的扩散模型

Diffusion Models With Learned Adaptive Noise

Heterogeneous graph neural networks (HGNNs) have recently shown impressive
capability in modeling heterogeneous graphs that are ubiquitous in real-world
applications. Due to the diversity of attributes of nodes in different types,
most existing models first align nodes by mapping them into the same
low-dimensional space. However, in this way, they lose the type information of
nodes. In addition, most of them only consider the interactions between nodes
while neglecting the high-order information behind the latent interactions
among different node features. To address these problems, in this paper, we
propose a novel heterogeneous graph model MULAN, including two major
components, i.e., a type-aware encoder and a dimension-aware encoder.
Specifically, the type-aware encoder compensates for the loss of node type
information and better leverages graph heterogeneity in learning node
representations. Built upon transformer architecture, the dimension-aware
encoder is capable of capturing the latent interactions among the diverse node
features. With these components, the information of graph heterogeneity, node
features and graph structure can be comprehensively encoded in node
representations. We conduct extensive experiments on six heterogeneous
benchmark datasets, which demonstrates the superiority of MULAN over other
state-of-the-art competitors and also shows that MULAN is efficient.

利用异构图神经网络建模了类型多样性节点，并通过两个关键组件，即类型感知编码器和维度感知编码器，解决了节点类型信息丢失和高阶信息损失问题，在编码节点表征时充分利用了图的异构性。实验证明了 MULAN 在六个异构数据集上的优越性和高效性。