论文标题
使用epoiriant变压器变化自动编码器对星系形态的半监督学习
Semi-supervised Learning of Galaxy Morphology using Equivariant Transformer Variational Autoencoders
论文作者
论文摘要
星系图像数量的增长速度要比这些星系被人类标记的速度快得多。但是，通过利用不断增长的未标记图像集中的信息，半监督学习可能是降低所需标签和提高分类准确性的有效方法。我们开发了一个具有均等变压器层的变异自动编码器（VAE），该层带有来自潜在空间的分类器网络。我们表明，这种新颖的体系结构可在Galaxy Zoo数据集上用于星系形态分类任务时，可以提高准确性。此外，我们证明，与退出方法相比，使用未标记的数据将分类器网络作为VAE的一部分进行预训练，并且标签较少。这种新颖的VAE有潜力通过减少人类标记工作来自动化星系形态分类。
The growth in the number of galaxy images is much faster than the speed at which these galaxies can be labelled by humans. However, by leveraging the information present in the ever growing set of unlabelled images, semi-supervised learning could be an effective way of reducing the required labelling and increasing classification accuracy. We develop a Variational Autoencoder (VAE) with Equivariant Transformer layers with a classifier network from the latent space. We show that this novel architecture leads to improvements in accuracy when used for the galaxy morphology classification task on the Galaxy Zoo data set. In addition we show that pre-training the classifier network as part of the VAE using the unlabelled data leads to higher accuracy with fewer labels compared to exiting approaches. This novel VAE has the potential to automate galaxy morphology classification with reduced human labelling efforts.
