前言：本文是作者在撰寫小論文過程中，從相關(guān)頂會論文中摘選出來的常用單詞不同的說法，從而避免論文文筆過于單調(diào)。本文將會不定期更新~歡迎小伙伴們在留言區(qū)補(bǔ)充哦！

1. 常用詞的N種說法

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 多種多樣的
• 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

2. 專有名詞

2.1 Image-to-Image Translation

2.1.1 Word

• stylised image 轉(zhuǎn)換后的風(fēng)格圖
• paired image 成對的圖片
• be transfered to
• a fixed target style
• multimodal translation 多遷移
• deconvolution layer 反卷積層
• 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 目標(biāo)域
• intermediate domain 中間域
• generalization ability 生成能力
• domainness variable 鄰域變量
• pixel level
• synthetic data 合成數(shù)據(jù)
• real scenario 真實場景
• be proportional to 和…成比例
• recover … from …
• be implemented with 被實施
• 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 語義約束

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. 常用短語

• draw increasing attention
• most existing works
• the benefit of
• training phase 訓(xùn)練階段
• inference phase 測試階段
• 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 …的開創(chuàng)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. 常用銜接語

• 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. 長句型

• 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

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

本文為作者原創(chuàng)，轉(zhuǎn)載需注明出處！

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