论文标题
使用VAE的小数据表中半监督学习的绩效分析
Performance Analysis of Semi-supervised Learning in the Small-data Regime using VAEs
论文作者
论文摘要
由于辐射问题，从生物样品中提取大量数据是不可行的，而小型数据制度中的图像处理是使用有限的数据时的关键挑战之一。在这项工作中，我们应用了一种名为“变量自动编码器”（VAE）的现有算法，该算法预先培训数据的潜在空间表示形式，以捕获小型数据制度输入的较低维度中的特征。微型潜在空间提供了可用于分类的恒定权重。在这里，我们将使用CIFAR-10数据集对VAE算法进行具有不同潜在空间大小的VAE算法分析。
Extracting large amounts of data from biological samples is not feasible due to radiation issues, and image processing in the small-data regime is one of the critical challenges when working with a limited amount of data. In this work, we applied an existing algorithm named Variational Auto Encoder (VAE) that pre-trains a latent space representation of the data to capture the features in a lower-dimension for the small-data regime input. The fine-tuned latent space provides constant weights that are useful for classification. Here we will present the performance analysis of the VAE algorithm with different latent space sizes in the semi-supervised learning using the CIFAR-10 dataset.
