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# 计算机代写|扩散模型代写Diffusion Model代考|ENGG3300 A CATEGORIZATION OF DIFFUSION MODELS

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## 计算机代写|扩散模型代写Diffusion Model代考|A CATEGORIZATION OF DIFFUSION MODELS

We categorize diffusion models into a multi-perspective taxonomy considering different criteria of separation. Perhaps the most important criteria to separate the models are defined by $(i)$ the task they are applied to, and $(i i)$ the input signals they require. Furthermore, as there are multiple approaches in formulating a diffusion model, (iii) the underlying framework is a another key factor for classifying diffusion models. Finally, the $(i v)$ data sets used during training and evaluation are also of high importance, because they provide the means to compare different models on the same task. Our categorization of diffusion models according to the criteria enumerated above is presented in Table 1.
In the reminder of this section, we present several contributions on diffusion models, choosing the target task as the primary criterion to separate the methods. We opted for this classification criterion as it is fairly well-balanced and representative for research on diffusion models, facilitating a quick grasping of related works by readers working on specific tasks. Although the main task is usually related to image generation, a considerable amount of work has been conducted to match and even surpass the performance of GANs on other topics, such as super-resolution, inpainting, image editing, image-to-image translation or segmentation.

## 计算机代写|扩散模型代写Diffusion Model代考|Unconditional Image Generation

The diffusion models presented below are used to generate samples in an unconditional setting. Such models do not require supervision signals, being completely unsupervised. We consider this as the most basic and generic setting for image generation.
3.1.1 Denoising Diffusion Probabilistic Models
The work of Sohl et al. [1] formalizes diffusion models as described in Section 2.1. The proposed neural network is based on a convolutional architecture containing multi-scale convolution.

Austin et al. [73] extend the approach of Sohl et al. [1] to discrete diffusion models, studying different choices for the transition matrices used in the forward process. Their results are competitive with previous continuous diffusion models for the image generation task.

Ho $e t$ al. [2] extend the work presented in [1], proposing to learn the reverse process by estimating the noise in the image at each step. This change leads to an objective that resembles the denoising score matching applied in [3]. To predict the noise in an image, the authors use the PixelCNN++ architecture, which was introduced in [68].

On top of the work proposed by Ho et al. [2], Nichol et al. [6] introduce several improvements, observing that the linear noise schedule is sub-optimal for low resolution. They propose a new option that avoids a fast information destruction towards the end of the forward process. Further, they show that it is required to learn the variance in order to improve the performance of diffusion models in terms of log-likelihood. This last change allows faster sampling, somewhere around 50 steps being required.

## 计算机代写|扩散模型代写Diffusion Model代考|Unconditional Image Generation

3.1.1 去噪扩散概率模型
Sohl 等人的工作。[1] 将扩散模型形式化，如第 2.1 节所述。所提出的神经网络基于包含多尺度卷积的卷积架构。

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