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Microbial communities are dynamic environments where cell lysis releases free DNA into the surroundings. Other cells can take up this extracellular DNA through a process known as transformation.When a cell incorporates this foreign DNA into its genome, resulting in genetic modification, the process is known as transformation. Cells capable of this process are termed competent. Competence can be natural, as observed in certain bacteria and archaea, or artificially induced in the...
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相关实验视频

Updated: Jan 28, 2026

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基于补丁的旋转变压器Hyperprior用于学习的图像压缩.

Sibusiso B Buthelezi1, Jules R Tapamo1

  • 1Discipline of Electrical, Electronic and Computer Engineering, University of KwaZulu-Natal, Durban 4041, South Africa.

Journal of imaging
|January 27, 2026
PubMed
概括

本研究介绍了一种混合学习图像压缩方法,使用基于CNN的变化自编码器 (VAE) 和Swin变压器. 这种新的方法通过有效地建模全球依赖关系,同时保持计算可行性,提高了压缩效率和视觉质量.

科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 图像处理 图像处理

背景情况:

  • 使用卷积神经网络 (CNN) 的传统学习图像压缩方法,由于局部受体场,难以捕捉潜在表示中的远程依赖性,从而限制了压缩效率.
  • 完全基于变压器的模型提供全球依赖性建模,但产生高计算成本,使他们不适合高分辨率图像压缩.

研究的目的:

  • 开发一种混合端到端学习图像压缩框架,克服现有的模型的局限性.
  • 在计算约束范围内有效地建模本地和全球上下文信息,以改进图像压缩.

主要方法:

  • 一个混合框架,将基于CNN的变化自编码器 (VAE) 与基于补丁的层次化的Swin变压器超前组合在一起.
  • 在Swin变压器中利用转移窗口自我注意力,以有效地捕捉本地和全球依赖.
  • 将模型与可微分量子化模块集成在一起,以实现速率扭曲目标的端到端联合优化.

主要成果:

  • 拟议的混合架构在标准数据集 (Kodak,JPEG AI,CLIC) 上实现了优越的速率扭曲性能.
  • 与基于CNN的率先验相比,在较低的比特率下表现出更好的压缩性能,具有更高的视觉质量.
  • 该模型学习了潜在表示的更准确的概率分布,改进了比特率估计,并实现了更紧的潜在表示.
关键词:
可微分量的量化模块.隐性变量的建模.优化速率扭曲的优化方法斯温变压器 超前变压器变化推理推理是变化的推理.

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结论:

  • 集成高效的变压器架构,如Swin变压器,到学习的图像压缩是有效的.
  • 混合方法为超越传统的基于CNN的设计的先进建模提供了可行的解决方案.
  • 这项工作通过平衡性能和计算效率来推进学习图像压缩领域.