Paper Citation Record · LEDGER

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising

As of 21 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2506.19434.

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pith.paper-citation-record.v1
2506.19434 v1

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Outbound references

Observation 752bf3c6-06ae-48ca-a34e-a552662f9d36 · outbound

This paper cites Morphological Classification of Galaxies Through Structural and Star Formation Parameters Using Machine Learning.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising Morphological Classification of Galaxies Through Structural and Star Formation Parameters Using Machine Learning

Reference 1

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This paper cites A benchmark analysis of saliency-based explainable deep learning methods for the morphological classification of radio galaxies.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising A benchmark analysis of saliency-based explainable deep learning methods for the morphological classification of radio galaxies

Reference 2

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This paper cites et al, 2011 , The EFIGI catalogue of 4458 nearby galaxies with detailed morphology, Astronomy & Astrophysics, 532, A74.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising et al, 2011 , The EFIGI catalogue of 4458 nearby galaxies with detailed morphology, Astronomy & Astrophysics, 532, A74

Reference 3

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Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising Unresolved cited work

Reference 4

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Observation 9e6bb367-6f1d-4ff7-812a-8ae9cbd25150 · outbound

This paper cites et al., 2022, A program to build E(N)-equivariant steerable CNNs.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising et al., 2022, A program to build E(N)-equivariant steerable CNNs

Reference 5

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Observation f6f64344-7cdc-44c4-bed1-d7c723637346 · outbound

This paper cites Robustness of deep learning algorithms in astronomy -- galaxy morphology studies.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising Robustness of deep learning algorithms in astronomy -- galaxy morphology studies

Reference 6

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Observation 69a7c5e6-e93f-4b71-b1e5-b9b846de8919 · outbound

This paper cites et al, 2022, Deepadversaries: examining the robustness of deep learning models for galaxy morphology classification.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising et al, 2022, Deepadversaries: examining the robustness of deep learning models for galaxy morphology classification

Reference 7

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This paper cites Group Equivariant Convolutional Networks.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising Group Equivariant Convolutional Networks

Reference 8

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Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising Unresolved cited work

Reference 9

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This paper cites al., 2021, ROGER: Reconstructing orbits of galaxies in extreme regions using machine learning techniques, MNRAS, Volume 500, Issue 2, pp.1784-1794.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising al., 2021, ROGER: Reconstructing orbits of galaxies in extreme regions using machine learning techniques, MNRAS, Volume 500, Issue 2, pp.1784-1794

Reference 10

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Observation 9e955a4c-6a0d-4fdf-86b4-67e3f8cd5cc1 · outbound

This paper cites G., et al.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising G., et al

Reference 11

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Observation f6689a28-c0d1-4e2a-b3e0-090c26474938 · outbound

This paper cites Machine Learning Workflow for Morphological Classification of Galaxies.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising Machine Learning Workflow for Morphological Classification of Galaxies

Reference 12

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This paper cites Robustness of Rotation-Equivariant Networks to Adversarial Perturbations.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising Robustness of Rotation-Equivariant Networks to Adversarial Perturbations

Reference 13

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This paper cites DAWN JWST Archive: Morphology from profile fitting of over 340 000 galaxies in major fields.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising DAWN JWST Archive: Morphology from profile fitting of over 340 000 galaxies in major fields

Reference 14

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This paper cites Intrinsic galaxy alignments in the KiDS-1000 bright sample: dependence on colour, luminosity, morphology, and galaxy scale.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising Intrinsic galaxy alignments in the KiDS-1000 bright sample: dependence on colour, luminosity, morphology, and galaxy scale

Reference 15

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Observation 49f1de4d-7db2-4a61-91af-d3a6e87fc626 · outbound

This paper cites Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches

Reference 16

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Observation 3564b499-5363-4140-8bff-3df6b5cf3fbf · outbound

This paper cites et al., 2022, Galaxy Morphology Classification with DenseNet, Journal of Physics: Conference Series, Volume 2402, Issue 1, id.012009, 11 pp.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising et al., 2022, Galaxy Morphology Classification with DenseNet, Journal of Physics: Conference Series, Volume 2402, Issue 1, id.012009, 11 pp

Reference 17

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This paper cites Morphological Feature Distances Among the Spectral Types of SDSS Galaxies.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising Morphological Feature Distances Among the Spectral Types of SDSS Galaxies

Reference 18

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This paper cites Auto-Encoding Variational Bayes.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising Auto-Encoding Variational Bayes

Reference 19

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Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising Unresolved cited work

Reference 20

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Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising Unresolved cited work

Reference 21

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Observation d974bbe9-8f2f-40c7-b49f-66ef89fbd90a · outbound

This paper cites Evaluating the Accuracy of Non-parametric Galaxy Morphological Indicator Measurements in the CSST Imaging Survey.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising Evaluating the Accuracy of Non-parametric Galaxy Morphological Indicator Measurements in the CSST Imaging Survey

Reference 22

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This paper cites Morphological Classification of Galaxies.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising Morphological Classification of Galaxies

Reference 23

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This paper cites S.; Khachatryan, H.; Yegorian, G.; Gurzadyan, V.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising S.; Khachatryan, H.; Yegorian, G.; Gurzadyan, V

Reference 24

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This paper cites et al., 2010, Galaxy Formation and Evolution, Cambridge University Press.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising et al., 2010, Galaxy Formation and Evolution, Cambridge University Press

Reference 25

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This paper cites E(2) Equivariant Neural Networks for Robust Galaxy Morphology Classification.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising E(2) Equivariant Neural Networks for Robust Galaxy Morphology Classification

Reference 27

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This paper cites et al., 2003, HYPERLEDA.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising et al., 2003, HYPERLEDA

Reference 28

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This paper cites Data-Efficient Classification of Radio Galaxies.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising Data-Efficient Classification of Radio Galaxies

Reference 30

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This paper cites The SRG/eROSITA all-sky survey: The morphologies of clusters of galaxies I: A catalogue of morphological parameters.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising The SRG/eROSITA all-sky survey: The morphologies of clusters of galaxies I: A catalogue of morphological parameters

Reference 31

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This paper cites Automatic Machine Learning Framework to Study Morphological Parameters of AGN Host Galaxies within $z < 1.4$ in the Hyper Supreme-Cam Wide Survey.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising Automatic Machine Learning Framework to Study Morphological Parameters of AGN Host Galaxies within $z < 1.4$ in the Hyper Supreme-Cam Wide Survey

Reference 33

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Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising Unresolved cited work

Reference 34

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This paper cites et al., 2022, Galaxy Zoo DECaLS: Detailed visual morphology measurements from volunteers and deep learning for 314000 galaxies, MNRAS, Volume 509, Issue 3, pp.3966-3988.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising et al., 2022, Galaxy Zoo DECaLS: Detailed visual morphology measurements from volunteers and deep learning for 314000 galaxies, MNRAS, Volume 509, Issue 3, pp.3966-3988

Reference 35

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This paper cites Conference on Neural Information Processing Systems (NeurIPS).

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising Conference on Neural Information Processing Systems (NeurIPS)

Reference 37

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This paper cites et al., 2019, Galaxy morphology classification with deep convolutional neural networks, Astrophysics and Space Science, Volume 364, Issue 4, id.

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising et al., 2019, Galaxy morphology classification with deep convolutional neural networks, Astrophysics and Space Science, Volume 364, Issue 4, id

Reference 39

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