在高维空间中学习基于能源的模型,具有多尺度的Denoising-Score匹配匹配
Zengyi Li1,2, Yubei Chen1,3, Friedrich T Sommer1,4,5
1Redwood Center for Theoretical Neuroscience, Berkeley, CA 94720, USA.
经过多尺度否定分数匹配训练的基于能源的模型 (EBM) 实现了对高维数据的高质量样本合成. 这种新的方法为EBM设定了新的基准,与生成对抗网络 (GAN) 竞争.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 基于能源的模型 (EBM) 是多功能工具,用于诸如样本合成和无声化等任务.
- 由于采样要求,EBM的标准最大概率培训是计算密集的.
- 排斥分数匹配提供了更快的训练,但历史上缺乏高质量的样本合成高维数据.
研究的目的:
- 分析和证明对高维EBM进行多噪声水平培训的必要性.
- 引入一个新的EBM,训练有多个规模的报销-分数匹配.
- 为EBM在生成任务中建立一个新的绩效基准.
主要方法:
- 对不同噪音水平的EBM进行培训动态的分析.
- 开发和实施一个多尺度无声分数匹配技术.
- 对高维数据集和图像绘制任务的实证评估.
主要成果:
- 用多个噪音级别进行训练对于有效的高维数据合成至关重要.
- 拟议的多尺度EBM实现了与GANs等最先进的生成模型相美的性能.
- 该模型在密度估计和图像绘制方面表现出强的性能.
结论:
- 多尺度无证分数匹配显著提高了EBM对高维度生成任务的能力.
- 拟议的EBM为现有的生成模型提供了有竞争力的替代方案.
- 这项工作推动了EBM在复杂数据生成和分析中的应用.
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