1 research outputs found
Unsupervised Star Galaxy Classification with Cascade Variational Auto-Encoder
The increasing amount of data in astronomy provides great challenges for
machine learning research. Previously, supervised learning methods achieved
satisfactory recognition accuracy for the star-galaxy classification task,
based on manually labeled data set. In this work, we propose a novel
unsupervised approach for the star-galaxy recognition task, namely Cascade
Variational Auto-Encoder (CasVAE). Our empirical results show our method
outperforms the baseline model in both accuracy and stability