Dragging 方式。这种编辑技术受到 DragGAN 的启发,用户给定若干组的 (起始点,终止点)对,图像编辑技术通过优化的方式将起始点附近的内容移动到终止点附近,同时保持编辑结果的保真度和语义合理性,典型的代表是 SDE-Drag[1] 和DragDiffusion[2]。 基于Dragging 的图像编辑示例 【优点】这种编辑方式的优点一方面是直观,另...
DenseDiffusion BoxDiff 近年来,图像生成研究取得了巨大进展。过去几年,GANs 是最先进的技术,其 latent space 和conditional inputs 已经得到了深入研究,以实现可控的修改和生成。文本条件自回归和扩散模型已经展示出惊人的图像质量和概念覆盖,这是由于它们更稳定的学习目标和基于网络图像-文本对数据的大规模训练所致。
论文翻译(扩散模型来了):Diffusion-Based Mel-Spectrogram Enhancement for Personalized Speech Synthesis with Found Data 利用发现的数据来创建合成声音是具有挑战性的,因为现实世界的录音通常包含各种类型的音频退化。解决这个问题的一种方法是使用增强模型对语音进行预增强,然后使用增强后的数据进行文本转语音(TTS)模型训...
In this section, we provide a brief overview of TSDiff, an ML model designed to learn the conditional distribution of 3D TS geometries given 2D reaction information presented as SMARTS55(see Fig.1a). TSDiff is based on the stochastic denoising diffusion method, where the model is trained to ...
34and exhibit greater stability in training than GAN-based models35regarding image generation and restoration tasks due to their distinctive diffusion-based training and inference schemes. Diffusion models also generate outputs with high-resolution details, which is critical for imaging intricate nanoscale ...
Diffusion-based kernel methods are commonly used for analyzing massive high dimensional datasets. These methods utilize a non-parametric approach to represent the data by using an affinity kernel that represents similarities, distances or correlations between data points. The kernel is based on a Marko...
Rethinking deep active learning for medical image segmentation: A diffusion and angle-based framework Introduce DifABAL, a one-shot active learning framework for medical image segmentation.Utilize diffusion model-based autoencoders to extract features from ... L Qu,Q Jin,K Fu,... - 《Biomedical...
Faster Non-Log-Concave Sampling via Diffusion-based Monte Carlo Abstract: Efficient sampling from complex non-log-concave distributions is a cornerstone of statistical computing and machine learning, yet it is challenged by stringe...
Optimal Demodulation of Reaction Shift Keying Signals in Diffusion-based Molecular Communication Networks We derive an optimal RSK demodulator using the continuous history of the number of complexes at the receiver as the input to the demodulator. We do that by first deriving a communication model whi...
一、Diffusion Probabilistic Models (DPMs) Diffusion-based generative models: forward/diffusion process:图中从右往左. 从x0经过好多个不同的q(xt|xt−1)到xT,相当于 VAE 中的 encoder. reverse process:图中从左往右. 从xT经过好多个不同的q(xt−1|xt)到x0,相当于 VAE 中的 decoder. (用pθ(xt...