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Conservation of Protein Domains02:26

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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
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相关实验视频

Updated: Jun 3, 2025

Visualizing Visual Adaptation
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无监督域适应的域导向条件扩散模型.

Yulong Zhang1, Shuhao Chen2, Weisen Jiang3

  • 1State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang, China.

Neural networks : the official journal of the International Neural Network Society
|January 8, 2025
PubMed
概括
此摘要是机器生成的。

域引导条件扩散模型 (DCDM) 通过生成现实的目标数据来提高深度学习模型在新任务上的性能. 这种方法可以改善无监督域名适应 (UDA),即使有重大域名转移和有限的数据.

关键词:
扩散模型的扩散模型.转移学习转移学习无监督的域名适应

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 深度学习模型在新情景的有限可转移性方面扎.
  • 无监督域适应 (UDA) 旨在通过学习域不变特征来弥合这一差距.
  • 现有的UDA方法面临挑战,因为大范围的域名转移和稀缺的目标数据.

研究的目的:

  • 提出一个新的域引导条件扩散模型 (Domain-guided Conditional Diffusion Model,简称DCDM),用于增强无监督域适应.
  • 为了生成高保真性目标域样本,以促进域名转移.
  • 解决UDA中显著的域名转移和有限的目标域数据所带来的局限性.

主要方法:

  • 开发了一个域导向条件扩散模型 (DCDM).
  • 集成的类信息来控制生成的样本标签.
  • 使用域分类器将生成的样本引导到目标域.

主要成果:

  • DCDM成功生成了高保真性目标域样本.
  • 拟议的方法显著提高了无监督域调整的性能.
  • 在各种基准上进行了广泛的实验,验证了DCDM的有效性.

结论:

  • DCDM提供了一种强大的方法来克服深度学习中的领域转移挑战.
  • 目标域样本的生成简化了转移学习过程.
  • DCDM表现出显著的业绩增长,推进了UDA的领域.