Transfer learning for galaxy morphology from one survey to another
Open accessAbstract
Deep learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorithms rely on large training samples of labelled galaxies (typically thousands of them). A key question for using DL classifications in future Big Data surveys is how much of the knowledge acquired from an existing survey can be exported to a new data set, i.e. if the features learned by the machines are meaningful for different data. We test the performance of DL models, trained with Sloan Digital Sky Survey (SDSS) data, on Dark Energy Survey (DES) using images for a sample of ∼5000 galaxies with a similar redshift distribution to SDSS. Applying the models directly to DES data provides a reasonable global accuracy (∼90 per cent), but small completeness and purity values. A fast domain adaptation step, consisting of a further training with a small DES sample of galaxies (∼500–300), is enough for obtaining an accuracy >95 per cent and a significant improvement in the completeness and purity values. This demonstrates that, once trained with a particular data set, machines can quickly adapt to new instrument characteristics (e.g. PSF, seeing, depth), reducing by almost one order of magnitude the necessary training sample for morphological classification. Redshift evolution effects or significant depth differences are not taken into account in this study.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
THE SEVENTH DATA RELEASE OF THE SLOAN DIGITAL SKY SURVEY
Abazajian, K. N., Adelman-McCarthy, J. K., Agüeros, M. A., Allam, S. S., Prieto, C. A., An, D., Anderson, K. S. J., Anderson, S. F., Annis, J., Bahcall, N. A., et al. (2009) · The Astrophysical Journal Supplement Series · doi:10.1088/0067-0049/182/2/543 · Cited by 5,341 · see citing works
Self-citationConvolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535302 · Cited by 3,277 · see citing works
Detailed Structural Decomposition of Galaxy Images
Peng, C. Y., Ho, L. C., Impey, C., & Rix, H. (2002) · The Astronomical Journal · doi:10.1086/340952 · Cited by 2,782 · see citing works
Eisenstein, D. J., Weinberg, D. H., Agol, E., Aihara, H., Prieto, C. A., Anderson, S. F., Arns, J. A., Aubourg, É., Bailey, S., Balbinot, E., et al. (2011) · The Astronomical Journal · doi:10.1088/0004-6256/142/3/72 · Cited by 2,182 · see citing works
Self-citationCANDELS: THE COSMIC ASSEMBLY NEAR-INFRARED DEEP EXTRAGALACTIC LEGACY SURVEY
Grogin, N. A., Kocevski, D. D., Faber, S. M., Ferguson, H. C., Koekemoer, A. M., Riess, A. G., Acquaviva, V., Alexander, D. M., Almaini, O., Ashby, M. L. N., et al. (2011) · The Astrophysical Journal Supplement Series · doi:10.1088/0067-0049/197/2/35 · Cited by 2,137 · see citing works
The Cosmic Evolution Survey (COSMOS): Overview
Scoville, N. Z., Aussel, H., Brusa, M., Capak, P. L., Carollo, C. M., Elvis, M., Giavalisco, M., Guzzo, L., Hasinger, G., Impey, C. D., et al. (2007) · The Astrophysical Journal Supplement Series · doi:10.1086/516585 · Cited by 2,005 · see citing works
Overview of the DESI Legacy Imaging SurveysPreprint
Dey, A. T., Schlegel, D. J., Lang, D., Blum, R., Burleigh, K., Fan, X., Findlay, J. R., Finkbeiner, D. P., Herrera, D., Juneau, S., et al. (2018) · HAL · Cited by 1,221 · see citing works
Self-citationThe Relationship between Stellar Light Distributions of Galaxies and Their Formation Histories
Conselice, C. J. (2003) · The Astrophysical Journal Supplement Series · doi:10.1086/375001 · Cited by 875 · see citing works
THE CLUSTER LENSING AND SUPERNOVA SURVEY WITH HUBBLE: AN OVERVIEW
Postman, M., Coe, D., Benítez, N., Bradley, L., Broadhurst, T. J., Donahue, M. E., Ford, H. C., Graur, O., Graves, G. J. M., Jouvel, S., et al. (2012) · The Astrophysical Journal Supplement Series · doi:10.1088/0067-0049/199/2/25 · Cited by 849 · see citing works
Self-citationRotation-invariant convolutional neural networks for galaxy morphology prediction
Dieleman, S., Willett, K. W., & Dambre, J. (2015) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stv632 · Cited by 781 · see citing works
The Dark Energy Survey: more than dark energy - an overview
Abbott, D., Abdalla, F., Aleksic, J., Allam, S., Amara, A., Bacon, D., Balbinot, E., Banerji, M., Bechtol, K., Benoit-Lévy, A., et al. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw641 · Cited by 766 · see citing works
Self-citationGalaxy Zoo 1: data release of morphological classifications for nearly 900 000 galaxies★
Lintott, C. J., Schawinski, K., Bamford, S. P., Slosar, A., Land, K., Thomas, D., Edmondson, E. M., Masters, K. L., Nichol, R. C., Raddick, M. J., et al. (2010) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2010.17432.x · Cited by 743 · see citing works
Self-citationTHE DEEP2 GALAXY REDSHIFT SURVEY: DESIGN, OBSERVATIONS, DATA REDUCTION, AND REDSHIFTS
Newman, J. A., Cooper, M. C., Davis, M., Faber, S. M., Coil, A. L., Guhathakurta, P., Koo, D. C., Phillips, A. C., Conroy, C., Dutton, A. A., et al. (2013) · The Astrophysical Journal Supplement Series · doi:10.1088/0067-0049/208/1/5 · Cited by 740 · see citing works
Flaugher, B., Diehl, H., Honscheid, K., Abbott, T., Alvarez, O., Angstadt, R., Annis, J., Antonik, M., Ballester, O., Beaufore, L., et al. (2004) · The Dark Energy Survey · doi:10.1088/0004-6256/150/5/150 · Cited by 710 · see citing works
Self-citationWillett, K. W., Lintott, C. J., Bamford, S. P., Masters, K. L., Simmons, B. D., Casteels, K. R. V., Edmondson, E. M., Fortson, L. F., Kaviraj, S., Keel, W. C., et al. (2013) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stt1458 · Cited by 659 · see citing works
Self-citationThe Horizon-AGN simulation: morphological diversity of galaxies promoted by AGN feedback
Dubois, Y., Peirani, S., Pichon, C., Devriendt, J., Gavazzi, R., Welker, C., & Volonteri, M. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw2265 · Cited by 494 · see citing works
Nair, P. B., & Abraham, R. G. (2010) · The Astrophysical Journal Supplement Series · doi:10.1088/0067-0049/186/2/427 · Cited by 470 · see citing works
Improving galaxy morphologies for SDSS with Deep Learning
Domínguez Sánchez, H., Huertas-Company, M., Bernardi, M., Tuccillo, D., & Fischer, J. L. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty338 · Cited by 382 · see citing works
Self-citationA CATALOG OF VISUAL-LIKE MORPHOLOGIES IN THE 5 CANDELS FIELDS USING DEEP LEARNING
Huertas-Company, M., Gravet, R., Cabrera-Vives, G., Pérez-González, P. G., Kartaltepe, J. S., Barro, G., Bernardi, M., Mei, S., Shankar, F., Dimauro, P., et al. (2015) · The Astrophysical Journal Supplement Series · doi:10.1088/0067-0049/221/1/8 · Cited by 285 · see citing works
Self-citationThe Horizon-AGN simulation: evolution of galaxy properties over cosmic time
Kaviraj, S., Laigle, C., Kimm, T., Devriendt, J., Dubois, Y., Pichon, C., Slyz, A. D., Chisari, E., & Peirani, S. (2017) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx126 · Cited by 230 · see citing works
Self-citationTHE LONG LIVES OF GIANT CLUMPS AND THE BIRTH OF OUTFLOWS IN GAS-RICH GALAXIES AT HIGH REDSHIFT
Bournaud, F., Perret, V., Renaud, F., et al. (2013) · The Astrophysical Journal · doi:10.1088/0004-637x/780/1/57 · Cited by 209 · see citing works
Galaxy Zoo: reproducing galaxy morphologies via machine learning★
Banerji, M., Lahav, O., Lintott, C. J., Abdalla, F. B., Schawinski, K., Bamford, S. P., Andreescu, D., Murray, P., Raddick, M. J., Slosar, A., et al. (2010) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2010.16713.x · Cited by 198 · see citing works
Self-citationMeert, A., Vikram, V., & Bernardi, M. (2014) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stu2333 · Cited by 198 · see citing works
Self-citationThe importance of minor-merger-driven star formation and black hole growth in disc galaxies
Kaviraj, S. (2014) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stu338 · Cited by 150 · see citing works
Self-citationRacca, G. D., Laureijs, R. J., Stagnaro, L., Salvignol, J.-C., Alvarez, J. L., Criado, G. S., Venancio, L. M. G., Short, A., Strada, P., Boenke, T., et al. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2230762 · Cited by 138 · see citing works
Using transfer learning to detect galaxy mergers
Ackermann, S., Schawinski, K., Zhang, C., Weigel, A. K., & Turp, M. D. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty1398 · Cited by 137 · see citing works
Galaxy Zoo: reproducing galaxy morphologies via machine learningPreprint
Banerji, M., Lahav, O., Lintott, C. J., Abdalla, F. B., Bamford, S. P., Andreescu, D., Murray, P., Jordan, M., Slosar, A., Szalay, A. S., et al. (2010) · Cited by 128 · see citing works
Self-citationThe long lives of giant clumps and the birth of outflows in gas-rich galaxies at high redshift
Bournaud, F., Perret, V., Renaud, F., et al. (2013) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-90724 · Cited by 118 · see citing works
LSST Dark Energy Science Collaboration, Marshall, P. J., Anguita, T., Bianco, F. B., Bellm, E. C., Brandt, N., Clarkson, W. I., Connolly, A. J., Gawiser, E., Ivezić, Ž., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.04058 · Cited by 115 · see citing works
Self-citationNeural computation as a tool for galaxy classification: methods and examples
Lahav, O., Nairn, A., Junior, L. S., et al. (1996) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/283.1.207 · Cited by 106 · see citing works
Self-citationGalaxies, Human Eyes, and Artificial Neural Networks
Lahav, O., Naim, A., Buta, R., Corwin, H. G., de Vaucouleurs, G., Dressler, A. M., Huchra, J. P., van den Bergh, S., Raychaudhury, S., Junior, L. S., et al. (1995) · Science · doi:10.1126/science.267.5199.859 · Cited by 100 · see citing works
Self-citationBelfiore, F., Maiolino, R., Bundy, K. A., Thomas, D., Maraston, C., Wilkinson, D. M., Sánchez, S. F., Bershady, M. A., Blanc, G. A., Bothwell, M. S., et al. (2015) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stv296 · Cited by 99 · see citing works
Self-citationDeep learning for galaxy surface brightness profile fitting
Tuccillo, D., Huertas-Company, M., Decencière, E., Velasco-Forero, S., Domínguez Sánchez, H., & Dimauro, P. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx3186 · Cited by 93 · see citing works
Meert, A., Vikram, V., & Bernardi, M. (2015) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stv2475 · Cited by 51 · see citing works
Self-citationSpotTune: Transfer Learning through Adaptive Fine-tuningPreprint
Guo, Y., Shi, H., Kumar, A., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.08737 · Cited by 43 · see citing works
Improving galaxy morphology with machine learning
Barchi, P. H., Costa, F., Sautter, R. A., Rosa, R., & de Carvalho, R. R. (2016) · Journal of Computational Interdisciplinary Sciences · doi:10.6062/jcis.2016.07.03.0114 · Cited by 13 · see citing works
Transfer Learning Using Classification Layer Features of CNNPreprint
Shermin, T., Murshed, M., Lu, G., & Teng, S. W. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.07459 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 10
- Barchi P. H., da Costa F. G., Sautter R., Moura T. C., Stalder D. H., Rosa R. R., de Carvalho R. R., 2017, preprint, ( arXiv:1705.06818 ) · arXiv
- Belfiore F., et al., 2015, MNRAS, 449, 867 Bengio Y., 2012, in Unsupervised and Transfer Learning - Work- shop held at ICML 2011, Bellevue, Washington, USA, July 2, 2011. pp 17-36, http://jmlr.csail.mit.edu/proceedings/ papers/v27/bengio12a.html
- Dey A., et al., 2018, preprint, ( arXiv:1804.08657 ) Dieleman S., Willett K. W., Dambre J., 2015, MNRAS, 450, 1441 · arXiv
- Drlica-Wagner A., et al., 2017, preprint,
- [arXiv:1708.01531](http://arxiv.org/abs/1708.01531) · arXiv
- Kornblith S., Shlens J., Le Q. V., 2018, CoRR, abs/1805.08974 · arXiv
- M., 2018, preprint, ( arXiv:1810.07857 ) · arXiv
- Tajbakhsh N., Shin J. Y., Gurudu S. R., Hurst R. T., Kendall C. B., Gotway M. B., Liang J., 2016, IEEE Transactions on Medical Imaging, 35, 1299
- Tuccillo D., Huertas-Company M., Decenci` ere E., Velasco-Forero S., Dom´ ınguez S´ anchez H., Dimauro P., 2017, preprint, ( arXiv:1711.03108 ) · arXiv
- Yosinski J., Clune J., Bengio Y., Lipson H., 2014, preprint, ( arXiv:1411.1792 ) · arXiv
Cited by
Sorted by citations
Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning
Walmsley, M., Smith, L., Lintott, C., Gal, Y., Bamford, S., Dickinson, H., Fortson, L., Kruk, S., Masters, K., Scarlata, C., et al. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz2816 · Cited by 160 · see citing works
Deep learning predictions of galaxy merger stage and the importance of observational realism
Bottrell, C., Hani, M. H., Teimoorinia, H., Ellison, S. L., Moreno, J., Torrey, P., Hayward, C. C., Thorp, M., Simard, L., & Hernquist, L. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz2934 · Cited by 113 · see citing works
Morpheus: A Deep Learning Framework for the Pixel-level Analysis of Astronomical Image Data
Hausen, R., & Robertson, B. E. (2020) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ab8868 · Cited by 110 · see citing works
Convolutional neural network identification of galaxy post-mergers in UNIONS using IllustrisTNG
Bickley, R. W., Bottrell, C., Hani, M. H., Ellison, S. L., Teimoorinia, H., Yi, K. M., Wilkinson, S., Gwyn, S., & Hudson, M. J. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab806 · Cited by 84 · see citing works
Self-supervised Representation Learning for Astronomical Images
Hayat, M. A., Stein, G., Harrington, P., Lukić, Z., & Mustafa, M. (2021) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/abf2c7 · Cited by 79 · see citing works
Identification of tidal features in deep optical galaxy images with convolutional neural networks
Sánchez, H. D., Martin, G., Damjanov, I., Buitrago, F., Huertas-Company, M., Bottrell, C., Bernardi, M., Knapen, J. H., Vega-Ferrero, J., Hausen, R., et al. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad750 · Cited by 28 · see citing works
Self-citationTanoglidis, D., Ćiprijanović, A., Drlica-Wagner, A., Nord, B., Wang, M. H. L. S., Amsellem, A. J., Downey, K., Jenkins, S., Kafkes, D., & Zhang, Z. (2021) · arXiv · doi:10.48550/arxiv.2109.08246 · Cited by 25 · see citing works
Self-citationScaling Laws for Galaxy Images
Walmsley, M., Bowles, M., Scaife, A. M. M., Makechemu, J. S., Gordon, A. J., Ferguson, A. M. N., Mann, R. G., Pearson, J., Popp, J. J., Bovy, J., et al. (2024) · arXiv · doi:10.48550/arxiv.2404.02973 · Cited by 2 · see citing works
“These projects use labelled images from a single telescope or, less frequently, apply transfer learning from one telescope to another [24, 25, 26].”
BackgroundXu, Z., Choi, Y., Yi, C., Park, C., Park, J., Park, H., & Song, S. (2026) · Aerospace · doi:10.3390/aerospace13030222
- Authors
- Domínguez Sánchez, H., Huertas-Company, M., Bernardi, M., Kaviraj, S., Fischer, J. L., Abbott, T. M. C., Abdalla, F. B., Annis, J., Avila, S., Brooks, D., Buckley-Geer, E., Carnero Rosell, A., Carrasco Kind, M., Carretero, J., Cunha, C. E., D’Andrea, C. B., da Costa, L. N., Davis, C., De Vicente, J., Doel, P., Evrard, A. E., Fosalba, P., Frieman, J., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Hollowood, D. L., Honscheid, K., Hoyle, B., James, D. J., Kuehn, K., Kuropatkin, N., Lahav, O., Maia, M. A. G., March, M., Melchior, P., Menanteau, F., Miquel, R., Nord, B., Plazas, A. A., Sanchez, E., Scarpine, V, Schindler, R., Schubnell, M., Smith, M., Smith, R. C., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Walker, A. R., & Zuntz, J.
- Affiliations
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
- Centre National de la Recherche Scientifique (FR)
- University of Hertfordshire (GB)
- Cerro Tololo Inter-American Observatory (CL)
- Rhodes University (ZA)
- Fermi National Accelerator Laboratory (US)
- University College London (GB)
- Fermi National Accelerator Laboratory, PO Box 500, Batavia, IL 60510, USA
- Laboratório Interinstitucional de e-Astronomia - LIneA, Rua Gal. José Cristino 77, Rio de Janeiro, RJ 20921-400, Brazil
- Department of Astronomy, University of Illinois at Urbana-Champaign, 1002 W. Green Street, Urbana, IL 61801, USA
- University of Illinois Urbana-Champaign (US)
- Institute for High Energy Physics (ES)
- Kavli Institute for Particle Astrophysics and Cosmology (US)
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (ES)
- University of Michigan (US)
- Institute of Space Sciences (ES)
- Institut dÉstudis Espacials de Catalunya (IEEC), E-08193 Barcelona, Spain
- University of California, Santa Cruz (US)
- Max Planck Institute for Extraterrestrial Physics (DE)
- Australian Astronomical Observatory (AU)
- National Observatory (BR)
- Princeton University (US)
- Institució Catalana de Recerca i Estudis Avançats (ES)
- Jet Propulsion Laboratory (US)
- Universidade Estadual de Campinas (UNICAMP) (BR)
- Oak Ridge National Laboratory (US)
- Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA
- ORCID
- Domínguez Sánchez, H
- Kaviraj, S
- Abbott, T M C
- Abdalla, F B
- Annis, J
- Avila, S
- Brooks, D
- Carretero, J
- D’Andrea, C B
- Davis, C
- Doel, P
- Evrard, A E
- Fosalba, P
- Frieman, J
- García-Bellido, J
- Gerdes, D W
- Gruen, D
- Gruendl, R A
- Gschwend, J
- Gutierrez, G
- Hartley, W G
- Hollowood, D L
- Honscheid, K
- Hoyle, B
- James, D J
- Kuehn, K
- Kuropatkin, N
- Lahav, O
- Maia, M A G
- March, M
- Melchior, P
- Menanteau, F
- Miquel, R
- Nord, B
- Plazas, A A
- Sanchez, E
- Schindler, R
- Schubnell, M
- Smith, M
- Smith, R C
- Soares-Santos, M
- Sobreira, F
- Suchyta, E
- Swanson, M E C
- Thomas, D
- Walker, A R
- Zuntz, J
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-12-28
- Volume
- 484
- Issue
- 1
- Pages
- 93-100
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W2907489335
- Cited by
- 91
- References
- 37
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 6 · 2023: 14 · 2022: 11 · 2021: 17 · 2020: 10 · 2019: 14 · 2018: 1
- FWCI
- 5.65
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Monthly Notices of the Royal Astronomical Society (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- University of Hertfordshire Research Archive (University of Hertfordshire) (submittedVersion)
- HAL (submittedVersion, other-oa)
- Americanae (AECID Library) (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
- arXiv (Cornell University)
- ETH Zürich Research Collection
- Funders
- National Research Council (AST-1816330, AST-1138766, AST-1536171)
- U.S. Department of Energy
- Ministerio de Ciencia e Innovación
- Ministerio de Economía y Competitividad (AYA2015-71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV-2016-0597, MDM-2015-0509)
- Science and Technology Facilities Council
- Higher Education Funding Council for England
- North Carolina Stroke Association
- University of Cambridge
- University of Chicago
- University College London
- University of Illinois
- University of Michigan
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- galaxy morphology, morphological classification, transfer learning, domain adaptation, deep learning, Sloan Digital Sky Survey, Dark Energy Survey, training sample size
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
