Optimizes ImageNet-pretrained AlexNet, UMAP, and a bagging multi-cluster voting scheme with K-means, Birch and Agg for unsupervised galaxy morphology classification, reporting improved stability and consistency with galaxy evolution expectations.
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2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
ResNet101 and InceptionV4 both reach approximately 90 percent accuracy on ten-class galaxy classification in Galaxy10 DECals, with ResNet101 superior on performance metrics.
citing papers explorer
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Robustness Analysis of USmorph: II. Optimizing Feature Extraction, Dimensionality Reduction, and Clustering for Unsupervised Galaxy Morphology Classification
Optimizes ImageNet-pretrained AlexNet, UMAP, and a bagging multi-cluster voting scheme with K-means, Birch and Agg for unsupervised galaxy morphology classification, reporting improved stability and consistency with galaxy evolution expectations.
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Classifying galaxies in the Galaxy10 DECals dataset using Inception and Residual CNNs
ResNet101 and InceptionV4 both reach approximately 90 percent accuracy on ten-class galaxy classification in Galaxy10 DECals, with ResNet101 superior on performance metrics.