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Morphology classification

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Rotation-invariant convolutional neural networks for galaxy morphology prediction

S. Dieleman, J. Dambre, K. Willett•Mon Mar 23 2015

A deep neural network model for galaxy morphology classification which exploits translational and rotational symmetry is developed in the context of the Galaxy Challenge, an international competition to build the best model for morphology classification based on annotated images from the Galaxy Zoo project.

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A Comparison of Deep Learning Architectures for Optical Galaxy Morphology Classification

Ezra Fielding, Clement N. Nyirenda, Mattia Vaccari•Sun Nov 07 2021

A comparison of deep learning architectures to determine which is best suited for optical galaxy morphology classification is offered, and DenseNet121 was found to produce the best results, in terms of accuracy, with a reasonable training time.

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Galaxy morphology prediction using capsule networks

R. Katebi, Yadi Zhou, R. Chornock, Razvan C. Bunescu•Fri Sep 21 2018

This work studied the performance of Capsule Network, a recently introduced neural network architecture that is rotationally invariant and spatially aware, on the task of galaxy morphology classification, and achieved promising results.

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Galaxy Morphology Classification using EfficientNet Architectures

Shreyas Kalvankar, Hrushikesh Pandit, Pranav Parwate•Sun Aug 30 2020

A fine-tuned architecture using EfficientNetB5 to classify galaxies into seven classes - completely round smooth, in-between smooth, cigarshaped smooth, lenticular, barred spiral, unbarred spiral and irregular is proposed.

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Self-supervised Representation Learning for Astronomical Images

G. Stein, Md. Abul Hayat, P. Harrington, Z. Luki'c, Mustafa A. Mustafa•Wed Dec 23 2020

It is shown that, without the need for labels, self-supervised learning recovers representations of sky survey images that are semantically useful for a variety of scientific tasks and can be directly used as features, or fine-tuned, to outperform supervised methods trained only on labeled data.

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Can semi-supervised learning reduce the amount of manual labelling required for effective radio galaxy morphology classification?

I. V. Slijepcevic, A. Scaife•Sun Nov 07 2021

It is found that although SSL provides additional regularisation, its performance degrades rapidly when using very few labels, and that using truly unlabelled data leads to a significant drop in performance.

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The Classification of Optical Galaxy Morphology Using Unsupervised Learning Techniques

Ezra Fielding, Clement N. Nyirenda, Mattia Vaccari•Sun Jun 12 2022

Unsupervised learning techniques are implemented to classify the Galaxy Zoo DECaLS dataset and could serve as the basis for a novel approach to generating more ”human-like” galaxy morphology classifications from unsupervised techniques.

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Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection

G. Perdue, A. 'Ciprijanovi'c, Ashia Lewis, K. Pedro, S. Madireddy, B. Nord, Stefan M. Wild•Mon Oct 31 2022

This work develops a Universal Domain Adaptation method DeepAstroUDA, capable of performing semi-supervised domain alignment that can be applied to datasets with different types of class overlap, and is capable of bridging the gap between two astronomical surveys.

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Morphology classification | State-of-the-Art