障碍不变的隐性神经表示
IEEE transactions on pattern analysis and machine intelligence
|February 15, 2024
概括
失调不变隐性神经表示 (DINER) 通过使用哈希表来克服光谱偏差来增强信号属性建模. 这种方法可以提高各种任务和网络架构的性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 隐式神经表示 (INRs) 通过坐标函数模型信号属性,证明对反向问题有效.
- INR的一个关键限制是网络训练期间的光谱偏差,限制了它们的表达力.
研究的目的:
- 为了解决隐含神经表示中的光谱偏差限制.
- 提出一种新的方法,即失调不变隐性神经表示 (DINER),用于增强信号表示.
主要方法:
- 通过哈希表来增强传统的INR骨干,以创建DINER.
- 重组输入信号坐标以使用哈希表将它们投射到一致的分布中.
- 改变哈希表宽度以控制表达力,对应于不同的几何元素 (1D,2D,3D).
主要成果:
- 晚餐通过使更好的信号建模显著减轻光谱偏差.
- DINER的表达力与哈希表宽度相比较大,涵盖了更多的几何元素.
- 在不同的INR骨干 (MLP,SIREN) 和任务 (图像/视频表示,相位检索,折射率恢复,神经辐射场优化) 中证明了泛化.
结论:
- 与最先进的算法相比,DINER 在质量和速度方面提供了卓越的性能.
- 拟议的方法有效地克服了INR中的光谱偏差.
- DINER为各种信号表示和反向问题提供了一种多功能和强大的方法.
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