Denoising diffusion probabilistic models (DDPMs) (Ho et al. 2020) have shown
impressive results on image and waveform generation in continuous state spaces.
Here, we introduce Discrete Denoising Diffusion Probabilistic Models (D3PMs),
diffusion-like generative models for discrete data that generalize the
multinomial diffusion model of Hoogeboom et al. 2021, by going beyond
corruption processes with uniform transition probabilities. This includes
corruption with transition matrices that mimic Gaussian kernels in continuous
space, matrices based on nearest neighbors in embedding space, and matrices
that introduce absorbing states. The third allows us to draw a connection
between diffusion models and autoregressive and mask-based generative models.
We show that the choice of transition matrix is an important design decision
that leads to improved results in image and text domains. We also introduce a
new loss function that combines the variational lower bound with an auxiliary
cross entropy loss. For text, this model class achieves strong results on
character-level text generation while scaling to large vocabularies on LM1B. On
the image dataset CIFAR-10, our models approach the sample quality and exceed
the log-likelihood of the continuous-space DDPM model.

本文提出了离散去噪扩散概率模型（D3PM），用于离散数据的扩散式生成模型，包括了仿真高斯核、嵌入空间中基于最邻近、引入吸收状态等过渡矩阵。研究表明过渡矩阵的选择对图像和文本领域下的生成模型结果至关重要，且提出的新损失函数在字符级别文本生成上取得了很好的效果。

离散状态空间中的结构去噪扩散模型

Structured Denoising Diffusion Models in Discrete State-Spaces

Text removal algorithms have been proposed for uni-lingual scripts with
regular shapes and layouts. However, to the best of our knowledge, a generic
text removal method which is able to remove all or user-specified text regions
regardless of font, script, language or shape is not available. Developing such
a generic text eraser for real scenes is a challenging task, since it inherits
all the challenges of multi-lingual and curved text detection and inpainting.
To fill this gap, we propose a mask-based text removal network (MTRNet). MTRNet
is a conditional adversarial generative network (cGAN) with an auxiliary mask.
The introduced auxiliary mask not only makes the cGAN a generic text eraser,
but also enables stable training and early convergence on a challenging
large-scale synthetic dataset, initially proposed for text detection in real
scenes. What's more, MTRNet achieves state-of-the-art results on several
real-world datasets including ICDAR 2013, ICDAR 2017 MLT, and CTW1500, without
being explicitly trained on this data, outperforming previous state-of-the-art
methods trained directly on these datasets.

提出了一种基于掩模的文字删除方法（MTRNet），作为一种有条件的对抗性生成网络（cGAN），配备辅助掩模使其成为通用文本擦除工具，该算法在多个真实数据集上实现了最先进的结果。