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We use a contrastive self-supervised learning framework to estimate distances to galaxies from their photometric images.
Photoelectric Magnitudes and Red-Shifts
W. A. Baum · 1962
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Photometric Redshifts of Galaxies
E. D. Loh and E. J. Spillar · 1986
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Slicing Through Multicolor Space: Galaxy Redshifts from Broadband Photometry
A. J. Connolly, I. Csabai, A. S. Szalay, D. C. Koo, R. G. Kron, and J. A. Munn · 1995
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A simple framework for contrastive learning of visual representations
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton · 2002
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Improved baselines with momentum contrastive learning
X. Chen, H. Fan, R. Girshick, and K. He · 2003
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Big self-supervised models are strong semi-supervised learners
T. Chen, S. Kornblith, K. Swersky, M. Norouzi, and G. Hinton · 2006
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Measuring reddening with sloan digital sky survey stellar spectra and recalibrating sfd
E. F. Schlafly and D. P. Finkbeiner · 2011
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The eleventh and twelfth data releases of the sloan digital sky survey: final data from sdss-iii
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Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Photometric redshifts for the SDSS Data Release 12
R. Beck, L. Dobos, T. Budavári, A. S. Szalay, and I. Csabai · 2016
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An automatic taxonomy of galaxy morphology using unsupervised machine learning
A. Hocking, J. E. Geach, Y. Sun, and N. Davey · 2018
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Photometric redshifts from sdss images using a convolutional neural network
J. Pasquet, E. Bertin, M. Treyer, S. Arnouts, and D. Fouchez · 2019
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The many flavours of photometric redshifts
M. Salvato, O. Ilbert, and B. Hoyle · 2019
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Self-training with Noisy Student improves ImageNet classification
Q. Xie, M.-T. Luong, E. Hovy, and Q. V. Le · 2019
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Momentum contrast for unsupervised visual representation learning
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick · 2020
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Fixing the train-test resolution discrepancy: FixEfficientNet
H. Touvron, A. Vedaldi, M. Douze, and H. Jégou · 2020
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A. v. d. Oord, Y. Li, and O. Vinyals · 2018
Cited alongside, same era.