Directional diffusion models Directional diffusion models 在前一节中,我们的研究揭示了一个导致普通扩散模型在图学习中性能不佳的关键因素:信噪比的迅速恶化。为了解决这一挑战,我们在前向扩散过程中引入了方向性噪声,即通过加入两个附加的约束条件,将各向同性高斯噪声转化为各向异性噪声。这两个约束条件对提高朴素扩散模
1. SceneDiff: Generative Scene-Level Image Retrieval with Text and Sketch Using Diffusion Models (未开源) 论文链接 利用text , sketch , 来对 图像进行检索 , 这里的做法是用对文本、草图和图像特征进行编码,并将其投影到基于扩散的共享空间中,约束草图和文本特征的去噪过程以生成潜在融合特征,同时使用预训...
Latent diffusion models are a class of probabilistic models used in machine learning and natural language processing (NLP). These models are particularly useful for tasks such as image generation, language modeling, and representation learning. In this article, we will provide a comprehensive overview...
We find that the intermediate feature maps of the pre-trained U-Net are diverse and have hidden discriminative representation properties. To unleash the potential of these latent properties of diffusion models, we present novel aggregation schemes. Firstly, we propose a novel attention mechanism for ...
为解决这个限制,引入结构引导的扩散模型对抗训练(Structure-guided Adversarial training of Diffusion Models, SADM)方法。迫使模型在每个训练批次中学习样本之间的流形结构。为确保模型捕捉到数据分布中真实的流形结构,提出一种新的结构判别器,通过对抗训练与扩散生成器进行游戏,区分真实的流形结构和生成的流形结构。 SAD...
The research on diffusion model is divided into the following three categories: (1) Classical Models; (2) Context-aware Models; (3) Deep learning-based diffusion Models. Table 1. Comparison of the properties of classical diffusion models. Classical diffusion modelCategoriesSocial networkSubmodularity...
002 (2023-11-29) Visual Anagrams Generating Multi-View Optical Illusions with Diffusion Models https://arxiv.org/pdf/2311.17919.pdf 003 (2023-11-29) SODA Bottleneck Diffusion Models for Representation Learning https://arxiv.org/pdf/2311.17901.pdf ...
Transformers, in particular, have driven much of the recent progress in and excitement about generative models. "The most recent breakthroughs in AI models have come from pre-training models on large amounts of data and using self-supervised learning to train models without explicit labels," ...
Generative models are a class of machine learning methods that learn a representation of the data they are trained on and model the data itself. They are typically based on deep neural networks. In contrast, discriminative models usually predict separate quantities given the data. ...
Over the past decade, the rise of deep learning models with their unparalleled ability to identify highly nonlinear maps has presented a potential alternative. When applied to nonlinear material property prediction, deep learning has served as an efficient forward approximation (replacing costly FE simul...