34 citations · 82 across the 30 of their papers we have counts for
32 papers
Robustness Analysis of USmorph: II. Optimizing Feature Extraction, Dimensionality Reduction, and Clustering for Unsupervised Galaxy Morphology Classification
Guanwen Fang, Xiaolei Yin, Yirui Zheng +5
We conduct a systematic robustness analysis of the unsupervised machine learning module within the hybrid framework \texttt{USmorph}. This module automatically discovers morphologi…
A robust morphological classification method for galaxies using dual-encoding contrastive learning and multi-clustering voting on JWST/NIRCam images
Xiaolei Yin, Guanwen Fang, Shiying Lu +3
The two-step galaxy morphology classification framework {\tt USmorph} successfully combines unsupervised machine learning (UML) with supervised machine learning (SML) methods. To e…
Robustness Analysis of USmorph: I. Generalization Efficiency of Unsupervised Strategies and Supervised Learning in Galaxy Morphological Classification
Shiwei Zhu, Guanwen Fang, Yao Dai +5
We conduct a systematic robustness analysis of the hybrid machine learning framework \texttt{USmorph}, which integrates unsupervised and supervised learning for galaxy morphologica…
An updated efficient galaxy morphology classification model based on ConvNeXt encoding with UMAP dimensionality reduction
Guanwen Fang, Shiwei Zhu, Jun Xu +5
We present an enhanced unsupervised machine learning (UML) module within our previous \texttt{USmorph} classification framework featuring two components: (1) hierarchical feature e…
Dual-coding contrastive learning based on ConvNeXt and ViT models for morphological classification of galaxies in COSMOS-Web
Shiwei Zhu, Guanwen Fang, Chichun Zhou +4
In our previous works, we proposed a machine learning framework named \texttt{USmorph} for efficiently classifying galaxy morphology. In this study, we propose a self-supervised me…
Time-EAPCR-T: A Universal Deep Learning Approach for Anomaly Detection in Industrial Equipment
Huajie Liang, Di Wang, Yuchao Lu +7
With the advancement of Industry 4.0, intelligent manufacturing extensively employs sensors for real-time multidimensional data collection, playing a crucial role in equipment moni…