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vomitory    
a. 呕吐的,使呕吐的
n. 催吐剂,大门

呕吐的,使呕吐的催吐剂,大门

vomitory
n 1: an entrance to an amphitheater or stadium

Vomitory \Vom"i*to*ry\, a. [L. vomitorious.]
Causing vomiting; emetic; vomitive.
[1913 Webster]


Vomitory \Vom"i*to*ry\, n.; pl. {Vomitories}.
1. An emetic; a vomit. --Harvey.
[1913 Webster]

2. [L. vomitorium.] (Arch.) A principal door of a large
ancient building, as of an amphitheater.
[1913 Webster]

Sixty-four vomitories . . . poured forth the immense
multitude. --Gibbon.
[1913 Webster]


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英文字典中文字典相关资料:


  • Crdoco: Pixel-level domain transfer with cross-domain . . .
    Yun Chun Chen, Yen-Yu Lin, Ming Hsuan Yang, Jia Bin Huang Unsupervised domain adaptation algorithms aim to transfer the knowledge learned from one domain to another (e g , synthetic to real images) The adapted representations often do not capture pixel-level domain shifts that are crucial for dense prediction tasks (e g , semantic segmentation)
  • 【Domain Adaptation】CrDoCo: Pixel-level Domain Transfer . . .
    论文导读：本文提出了一种新的域适应图像分割算法。 其指导思想是通过利用图像对图像的转换方法进行数据增强，虽然不同域之间的转换图像可能在风格上有所不同，但它们的预测结果应该是一致的。 无监督领域 自适应算法 目的是将学到的知识从一个数据域转移到另一个数据域 (例如，从合成图像到真实图像)。 深度网络 的训练需要大量是的数据，然而，对于许多密集的预测任务 (如语义分割、光流估计和 深度预测)，使用像素级标注来收集大规模和多样化的数据集是困难的，因为标注过程通常是昂贵的。 因此，开发能够将学到的知识从一个标记数据集 (即源域)转移到另一个未标记数据集 (即 目标域)的算法变得越来越重要。 然而，由于域转移问题 (即 源数据集 和目标数据集之间的域差距)，学习的模型往往不能很好地推广到新的 数据集。
  • CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency
    This repository contains the code for the paper CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency Unsupervised domain adaptation algorithms aim to transfer the knowledge learned from one domain to another (e g , synthetic to real images)
  • CVPR 2019 Open Access Repository
    Yun-Chun Chen, Yen-Yu Lin, Ming-Hsuan Yang, Jia-Bin Huang; Proceedings of the IEEE CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp 1791-1800 Abstract Unsupervised domain adaptation algorithms aim to transfer the knowledge learned from one domain to another (e g , synthetic to real images)
  • Bidirectional domain mixup for domain adaptive semantic segmentation . . .
    This paper systematically studies the impact of mixup under the domain adaptive semantic segmentation task and presents a simple yet effective mixup strategy called Bidirectional Domain Mixup (BDM) In specific, we achieve domain mixup in two-step: cut and paste
  • 2019 IEEE CVF Conference on Computer Vision and Pattern Recognition . . .
    Iterative Projection and Matching: Finding Structure-Preserving Representatives and Its Application to Computer Vision pp 5409-5418
  • C. -Y. Lin | IEEE Xplore Author Details
    Chen-Ying Lin is currently pursuing the M S degree with the Institute and Undergraduate Program of Electro-Optical Engineering, National Taiwan Normal University, Taipei, Taiwan His research focuses on FinFET and GAAFET
  • Evaluation of Chen et al. : Overexpression of Protein Complexes and . . .
    In this manuscript, Chen et al (2019) investigate the causes of fitness defects in aneuploid yeast strains and human cancers Previous work had suggested that imbalances in protein complexes are a significant cause of fitness defects in aneuploid cells The classic example studied in this context is alpha and beta tubulin
  • Crdoco: Pixel-level domain transfer with cross-domain consistency
    In this paper, we present a novel pixel-wise adversarial domain adaptation algorithm By leveraging image-to-image translation methods for data augmentation, our key insight is that while the translated images between domains may differ in styles, their predictions for the task should be consistent
  • CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency
    In this paper, we present a novel pixel-wise adversar-ial domain adaptation algorithm By leveraging image-to-image translation methods for data augmentation, our key insight is that while the translated images between domains may differ in styles, their predictions for the task should be consistent





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