本章节既可以叫 Methods（加不加s都行），或者叫 Our Approach
多强调你的方法的特点，与众不同之处，让审稿人一定能看到。
| 第一句：预热一下 |
| We now present our xx framework for xx. |
| In this section, we provide a more detailed description of the presented approach, xxx. |
| Given xx, we aim to achieve xxx (你文章是干什么的) |
| Our goal is to ... |
| This section introduces the overall working mechanism and specific technical implementations of the proposed xx. |
| 如果你的方法由多个部分构成的话 |
| Our xx process is split into two parts: a xx branch, which xxx, and a xx branch that xxx. |
| 章节结构安排 |
| In the following, we first introduce xx in Sec. 3.1, before proceeding to xx in Sec 3.2. |
| The organization of this section is as follows: We introduce xx in Sec. 3.1, including xx. In Sec. 3.2, we first describe xx. Then we present xx. |
| To begin with, we review the basic idea and pipeline of xxx. |
| To facilitate understanding, we begin with an overview of our framework xx |
插补一些有用的连词：Thereafter
| 最后一句：介绍核心的流程图 |
| Fig. x gives an overview of the training pipeline. |
| Our model is illustrated in Fig. x. |
| In essence, our method extends, ..., as demonstrated in Fig. 1 |
| Fig.1 depicts xx |
| Fig.1 shows the framework of the proposed method. |
| schematic illustrating |
比较具体，这里就不再过多介绍。
| 训练细节 |
| We inherit the training procedure from xx with minimal changes. |
| The optimization is performed by Adam with learning rate of 0.002 and betas of 0 and 0.99 |
| Further details can be found in the source code. |
| 有的时候会为了简单起见，省略一些符号标记 |
| for the sake of simplicity, we |
| For ease of notation, we will use latent codes c to denote the concatenation of all latent variables ci. |
| 对公式的描述 |
| where λ is a positive constant trading off the importance of the first and second terms of the loss |
| 一些不错的说辞 |
| we want to avoid investing effort into a component that is not a part of the actual solution. |
| 定义符号 |
| We use $\mathbf{x}$ to denote xx, and $\theta$ to represent the learnable parameters. |
| Let $\mathcal{X}$ denote the input space and $\mathcal{Y}$ the output space. |
| where $\nabla_{\mathbf{x}}$ is the gradient w.r.t. $\mathbf{x}$, and the superscript $^\top$ denotes transposition. |
| Here we omit the subscript $t$ for simplicity. |
| 介绍损失函数 |
| The total loss is defined as: $\mathcal{L} = \mathcal{L}{\text{rec}} + \lambda \mathcal{L}{\text{reg}}$ |
| where $\lambda$ is a positive constant trading off the importance of the reconstruction and regularization terms. |
| We optimize the following objective: ... |
| The loss function consists of two terms: a [X] loss and a [Y] loss. |
| 描述操作/变换 |
| We formulate [task] as [optimization problem / mapping / minimization of]. |
| Given [input], we aim to [predict / estimate / recover] [output]. |
| This can be written as: $\mathbf{y} = f_\theta(\mathbf{x})$
|
| Formally, we define [X] as follows: |
| The [module] takes [input] as input and outputs [output]. |
| 推导/化简 |
| Substituting Eq. (X) into Eq. (Y), we obtain ... |
| This follows directly from [prior result / assumption]. |
| By rearranging terms, we get ... |
| Taking the derivative w.r.t. $\theta$, we have ... |
| In the limit of [X → ∞ / X → 0], Eq. (Y) reduces to ... |