Recently, diffusion probabilistic models have attracted attention in
generative time series forecasting due to their remarkable capacity to generate
high-fidelity samples. However, the effective utilization of their strong
modeling ability in the probabilistic time series forecasting task remains an
open question, partially due to the challenge of instability arising from their
stochastic nature. To address this challenge, we introduce a novel
Multi-Granularity Time Series Diffusion (MG-TSD) model, which achieves
state-of-the-art predictive performance by leveraging the inherent granularity
levels within the data as given targets at intermediate diffusion steps to
guide the learning process of diffusion models. The way to construct the
targets is motivated by the observation that the forward process of the
diffusion model, which sequentially corrupts the data distribution to a
standard normal distribution, intuitively aligns with the process of smoothing
fine-grained data into a coarse-grained representation, both of which result in
a gradual loss of fine distribution features. In the study, we derive a novel
multi-granularity guidance diffusion loss function and propose a concise
implementation method to effectively utilize coarse-grained data across various
granularity levels. More importantly, our approach does not rely on additional
external data, making it versatile and applicable across various domains.
Extensive experiments conducted on real-world datasets demonstrate that our
MG-TSD model outperforms existing time series prediction methods.

引入了一种新颖的多粒度时间序列扩散（MG-TSD）模型，通过利用数据中的内在粒度级别作为中间扩散步骤的给定目标来指导扩散模型的学习过程，实现了最先进的预测性能。

MG-TSD: 多粒度时间序列扩散模型的引导式学习过程

MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided  Learning Process

Transfer learning from supervised ImageNet models has been frequently used in
medical image analysis. Yet, no large-scale evaluation has been conducted to
benchmark the efficacy of newly-developed pre-training techniques for medical
image analysis, leaving several important questions unanswered. As the first
step in this direction, we conduct a systematic study on the transferability of
models pre-trained on iNat2021, the most recent large-scale fine-grained
dataset, and 14 top self-supervised ImageNet models on 7 diverse medical tasks
in comparison with the supervised ImageNet model. Furthermore, we present a
practical approach to bridge the domain gap between natural and medical images
by continually (pre-)training supervised ImageNet models on medical images. Our
comprehensive evaluation yields new insights: (1) pre-trained models on
fine-grained data yield distinctive local representations that are more
suitable for medical segmentation tasks, (2) self-supervised ImageNet models
learn holistic features more effectively than supervised ImageNet models, and
(3) continual pre-training can bridge the domain gap between natural and
medical images. We hope that this large-scale open evaluation of transfer
learning can direct the future research of deep learning for medical imaging.
As open science, all codes and pre-trained models are available on our GitHub
page this https URL

通过对比自然图像与医学图像，本论文通过大规模评估了不同图像预训练模型在医学图像分析领域的可迁移性，提出了将自然图像模型迁移到医学图像中的实用方法，并发现新的关键性能：针对医学分割任务 fine-grained 数据预训练模型具有独特的局部特征表示，自我监督的 ImageNet 模型比有监督的 ImageNet 更有效地学习整体特征，连续预训练可以缩小自然图像与医学图像领域之间的跨度，有望引导深度学习在医学图像领域的未来研究。