论文标题
从随机采样图像中估算视觉信息的困难
Difficulty in estimating visual information from randomly sampled images
论文作者
论文摘要
在本文中，我们评估了降低降低方法的难度，这些方法在估计尺寸缩小图像的原始图像上的视觉信息方面进行了评估。最近，降低尺寸一直引起人们的注意，这不仅是减少随机变量的数量，还可以保护视觉信息以进行隐私的机器学习。由于原因，讨论了估计视觉信息的困难。特别地，将用于隐私机器学习提出的随机抽样方法与典型的降低方法进行了比较。在图像分类实验中，随机抽样方法不仅具有很高的难度，而且还可以与其他维度降低方法相媲美，同时保持空间信息不变的属性。
In this paper, we evaluate dimensionality reduction methods in terms of difficulty in estimating visual information on original images from dimensionally reduced ones. Recently, dimensionality reduction has been receiving attention as the process of not only reducing the number of random variables, but also protecting visual information for privacy-preserving machine learning. For such a reason, difficulty in estimating visual information is discussed. In particular, the random sampling method that was proposed for privacy-preserving machine learning, is compared with typical dimensionality reduction methods. In an image classification experiment, the random sampling method is demonstrated not only to have high difficulty, but also to be comparable to other dimensionality reduction methods, while maintaining the property of spatial information invariant.
