N way to write

1. 常用词的N种说法

1.1 Besides

  • Moreover
  • In addition

1.2 Get

  • obtain

1.3 Show

  • exhibit
  • demonstrate
  • present

1.4 Improve

  • be boosted to

1.5 Compare

  • In contrast

1.6 Can

  • allow to
  • attempt to
  • be able to
  • be exploited for
  • play the role of
  • be capable of

1.7 Many

  • diverse 多种多样的
  • multiple

1.8 Motivated by

  • Inspired by

1.9 Suppose

  • assume

1.10 Use

  • utilize
  • employ
  • apply … to …
  • enforce
  • be easily integrated with
  • deploy … as …
  • be taken as
  • be employed as

1.11 solve

  • address

1.12 define

  • refer to sth as sth
  • sth be identical to sth
  • be represented as

1.13 for example

  • for instance
  • take … as an example

1.14 come from

  • drift from

1.15 aim to

  • for the purpose of

1.16 without

  • dispensing with

2. 专有名词

2.1 Image-to-Image Translation

2.1.1 Word

  • stylised image 转换后的风格图
  • paired image 成对的图片
  • be transfered to
  • a fixed target style
  • multimodal translation 多迁移
  • deconvolution layer 反卷积层
  • stable training
  • uniform sampling
  • randomly sampled from

2.1.2 Advantage

  • deterministic one-to-one mapping transfer a soure image into the target style
  • these works can be divided into two categories according to the controllabiliy of the target styles
  • transfer the images in S into the style of T
  • transfer source images into the target style
  • By jointly optimizing all modules, CycleGAN model is able to transfer source images into the target sytle and v.v.
  • We take the S->T direction as an example, and the other direction can be similarly applied.
  • After the model is learnt, source images can only be translated to a fixed style

2.1.3 Disadvantage

  • Most previous studies for GAN-baed Image-to-Image translation methods rely on various forms of cycle-consistency$^{[1]}$.

2.2 Domain Adaption

2.2.1 Word

  • source domain 源域
  • target domain 目标域
  • intermediate domain 中间域
  • generalization ability 生成能力
  • domainness variable 邻域变量
  • pixel level
  • synthetic data 合成数据
  • real scenario 真实场景
  • be proportional to 和…成比例
  • recover … from …
  • be implemented with 被实施
  • spread along … from … to …
  • cross-domain semantic segmentation problem
  • a mixture of different target styles
  • shift from … to …
  • domain gap
  • domain alignment
  • distribution difference
  • semantic constraint 语义约束

2.2.2 Advantage

  • Domain adaptation aims to utilize a labeled source domain to learn a model that performs well on an unlabeled target domain
  • Domain generalization aims to learn a model that could be generalized to an unseen target domain by using multiple labeled source domains
  • align the image distributions for two domains
  • Object function of GAN can be seen as a lower bound of the Hessen-Shannon divergence
  • translate each source image into an arbitrary intermediate domain
  • Unsupersived domain adaption seeks to adapt the model trained on the source domain to the target domain$^{[1]}$.
  • Self-ensembling is composed of a teacher and a student network, where the student is compelled to produce consistent predictions provided by the teacher on target data$^{[1]}$.

2.2.3 Disadvantage

  • require a large amount of data with pixel-level annotations
  • The adversarial loss may trigger a negative transfer, which aligns the target feature with the source feature in an incorrect semantic category$^{[1]}$.

2.3 Style Transfer

2.3.1 Word

  • style patterns
  • the iterative optimization process
  • feedforward methods
  • insufficient visual quality
  • global feature statistics
  • in the feature space
  • encoder-decoder module
  • global color distribution
  • texture
  • local style patterns
  • bush strokes
  • receptive field

2.3.2 Sentence

  • Arbitrary style transfer aims to synthesize a content image with the style of an image to create a third image that has never been seen before.
  • The ultimate goal of arbitrary style transfer is to simultaneously achieve and preserve generation, quality, and efficiency.
  • We use the encoder(a pre-trained VGG-19) to compute the loss function for training …

3. 常用短语

  • draw increasing attention
  • most existing works
  • the benefit of
  • training phase 训练阶段
  • inference phase 测试阶段
  • be injected into 被注入
  • perform… by …
  • network structure
  • tent to
  • at the beginning
  • at the end
  • each of sth
  • computer vision tasks
  • achieve good performance
  • fill the gap between .. and …
  • conduct experiments on
  • training policy
  • verify the ability of
  • as expectedoop
  • give comparable result
  • by further using
  • from the first column, …
  • find it challenging to …
  • the seminal work of …的开创性工作
  • computational cost of …
  • computationally expensive operations
  • the proposed model
  • network structure
  • experimental settings
  • less appealing
  • run time performance
  • in real time
  • be compelled to 被迫

3.1 Solve the problem

  • To adress this challenging issue
  • To overcome this limitation

4. 常用衔接语

  • on one hand…, on the other hand
  • Unlike the above work
  • Our work is partialy inspired by
  • Formally, …
  • In particular, …
  • In this way, …
  • When …., …; and when, …
  • no longer aim to …, but to …
  • Specifically
  • Due to the usage of …
  • With regard to …
  • The motivation is as follows, …
  • Accordingly, …
  • In other words, …
  • In this section, …
  • It can be observed that
  • More interestingly, …
  • As discussed in
  • Note, …
  • In this paper, …
  • Experimental results demonstrate that …
  • Significant efforts have been made to …
  • Despite valuable efforts
  • Despite recent advances, …
  • In many cases, …
  • Similar to …

5. 长句型

  • Specifically, we introduce two discriminators, $D_S(x)$ to distinguish $M^{(z)}$ and S, and $D_T(x)$ to distinguish $M^{(z)}$ and T, respectively.
  • … in two settings. In the first setting, … . In the second setting, … .
  • Two cases are considered, … . For the first case, … . For the second case, … .
  • … in two scenarios. Firstly, … . Secondly, … .

References

  1. Choi, Jaehoon & Kim, Taekyung & Kim, Changick. (2019). Self-Ensembling with GAN-based Data Augmentation for Domain Adaptation in Semantic Segmentation.