{"paper":{"title":"Transfer learning for galaxy morphology from one survey to another","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.GA","authors_text":"A. A. Plazas, A. Carnero Rosell, A. E. Evrard, A. R. Walker, B. Hoyle, B. Nord, C. B. D'Andrea, C. Davis, C. E. Cunha, D. Brooks, D. Gruen, D. J. James, D. L. Hollowood, D. Thomas, D. W. Gerdes, E. Buckley-Geer, E. Gaztanaga, E. Sanchez, E. Suchyta, F. B. Abdalla, F. Menanteau, F. Sobreira, G. Gutierrez, G. Tarle, H. Dom\\'inguez S\\'anchez, J. Annis, J. Carretero, J. De Vicente, J. Frieman, J. Garc\\'ia-Bellido, J. Gschwend, J. L. Fischer, J. Zuntz, K. Honscheid, K. Kuehn, L. N. da Costa, M. A. G. Maia, M. Bernardi, M. Carrasco Kind, M. E. C. Swanson, M. Huertas-Company, M. March, M. Schubnell, M. Smith, M. Soares-Santos, N. Kuropatkin, O. Lahav, P. Doel, P. Fosalba, P. Melchior, R. A. Gruendl, R. C. Smith, R. Miquel, R. Schindler, S. Avila, S. Kaviraj, T. M. C. Abbott, V. Scarpine, W. G. Hartley","submitted_at":"2018-07-02T17:59:58Z","abstract_excerpt":"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 dataset, 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) dat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.00807","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}