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In this work, we apply self-supervised learning with instance differentiation to learn a robust, multi-purpose representation for image analysis of resolved extragalactic continuum images.
The Morphology of Extragalactic Radio Sources of High and Low Luminosity, Monthly Notices of the Royal Astronomical Society
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The FIRST Survey: Faint Images of the Radio Sky at Twenty Centimeters, ApJ
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The MeerKAT international GHz tiered extragalactic exploration (MIGHTEE) survey, in Proceedings of Science
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The MeerKAT radio telescope, Proceedings of Science
Jonas, J. L., 2016 · 2016
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Classifying radio galaxies with convolutional neural network
Aniyan, A. K. & Thorat, K., 2017 · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results, Advances in Neural Information Processing Systems
Tarvainen, A. & Valpola, H., 2017 · 2017
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Radio Galaxy Zoo: Compact and extended radio source classification with deep learning, Monthly Notices of the Royal Astronomical Society
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UMAP: Uniform manifold approximation and projection for dimension reduction, arXiv
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A Theoretical Analysis of Contrastive Unsupervised Representation Learning, in ICML
Arora, S., Khandeparkar, H., Khodak, M., Plevrakis, O., & Saunshi, N., 2019 · 2019
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MixMatch: A holistic approach to semi-supervised learning, in Neural Information Processing Systems (NeurIPS)
Berthelot, D., Carlini, N., Goodfellow, I., Oliver, A., Papernot, N., & Raffel, C., 2019 · 2019
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Revisiting the fanaroff–riley dichotomy and radio-galaxy morphology with the LOFAR two-metre sky survey (LoTSS), Monthly Notices of the Royal Astronomical Society
Mingo, B., Croston, J. H., Hardcastle, M. J., Best, P. N., Duncan, K. J., Morganti, R., Rottgering, H. J., Sabater, J., Shimwell, T. W., Williams, W. L., Brienza, M., Gurkan, G., Mahatma, V. H., Morabito, L. K., Prandoni, I., Bondi, M., Ineson, J., & Mooney, S., 2019 · 2019
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A Simple Framework for Contrastive Learning of Visual Representations, in The International Conference on Machine Learning (ICML
Chen, T., Kornblith, S., Norouzi, M., & Hinton, G., 2020 · 2020
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Exploring Simple Siamese Representation Learning, in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Chen, X. & He, K., 2020 · 2020
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Domain adaptation techniques for improved cross-domain study of galaxy mergers, in Machine Learning and the Physical Sciences - Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS)
Ćiprijanović, A., Kafkes, D., Jenkins, S., Downey, K., Perdue, G. N., Madireddy, S., Johnston, T., & Nord, B., 2020 · 2020
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A Concise Review of Transfer Learning, Proceedings - 2020 International Conference on Computational Science and Computational Intelligence (CSCI)
Farahani, A., Pourshojae, B., Rasheed, K., & Arabnia, H. R., 2020 · 2020
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Bootstrap your own latent a new approach to self-supervised learning, in Advances in Neural Information Processing Systems
Grill, J. B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Pires, B. A., Guo, Z. D., Azar, M. G., Piot, B., Kavukcuoglu, K., Munos, R., & Valko, M., 2020 · 2020
Cited alongside, same era.
Radio galaxies and feedback from AGN jets, New Astronomy Reviews
Hardcastle, M. J. & Croston, J. H., 2020 · 2020
Cited alongside, same era.
Estimating Galactic Distances From Images Using Self-supervised Representation Learning, Third Workshop on Machine Learning and the Physical Sciences (35th Conference on Neural Information Processing Systems; NeurIPS2020)
Hayat, M. A., Harrington, P., Stein, G., Lukić, Z., & Mustafa, M., 2020 · 2020
Cited alongside, same era.
Combining lofar and apertif data for understanding the life cycle of radio galaxies, Galaxies
Morganti, R., Jurlin, N., Oosterloo, T., Brienza, M., Orrú, E., Kutkin, A., Prandoni, I., Adams, E. A., Dénes, H., Hess, K. M., Shulevski, A., van der Hulst, T., & Ziemke, J., 2021 · 2021
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Meta Pseudo Labels, IEEE Conference on Computer Vision and Pattern Recognition
Pham, H., Dai, Z., Xie, Q., Luong, M.-T., & Le, Q. V., 2021 · 2021
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Radio Galaxy Classification: #Tags, Not Boxes, Galaxies 2021, Vol. 9, Page 85
Rudnick, L., 2021 · 2021
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Fanaroff–Riley classification of radio galaxies using group-equivariant convolutional neural networks, Monthly Notices of the Royal Astronomical Society
Scaife, A. M. M. & Porter, F., 2021 · 2021
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Can semi-supervised learning reduce the amount of manual labelling required for effective radio galaxy morphology classification?, NeurIPS 2021: Machine Learning and the Physical Sciences Workshop
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Momentum Contrast for Unsupervised Visual Representation Learning, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R., 2020 · 2020
Cited alongside, same era.
A Survey on Contrastive Self-Supervised Learning, Technologies
Jaiswal, A., Babu, A. R., Zadeh, M. Z., Banerjee, D., & Makedon, F., 2020 · 2020
Cited alongside, same era.
FixMatch: Simplifying semi-supervised learning with consistency and confidence, in Advances in Neural Information Processing Systems
Sohn, K., Berthelot, D., Li, C. L., Zhang, Z., Carlini, N., Cubuk, E. D., Kurakin, A., Zhang, H., & Raffel, C., 2020 · 2020
Cited alongside, same era.
What makes for good views for contrastive learning?, in Advances in Neural Information Processing Systems
Tian, Y., Sun, C., Poole, B., Krishnan, D., Schmid, C., & Isola, P., 2020 · 2020
Cited alongside, same era.
A survey on semi-supervised learning, Machine Learning
van Engelen, J. E. & Hoos, H. H., 2020 · 2020
Cited alongside, same era.
CNN architecture comparison for radio galaxy classification, Monthly Notices of the Royal Astronomical Society
Becker, B., Vaccari, M., Prescott, M., & Grobler, T., 2021 · 2021
Cited alongside, same era.
On the Opportunities and Risks of Foundation Models, CoRR
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J. Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D. E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P. W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X. L., Li, X., Ma, T., Malik, A., Manning, C. D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J. C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J. S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A. W., Tramèr, F., Wang, R. E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S. M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., & Liang, P., 2021 · 2021
Cited alongside, same era.
Attention-gating for improved radio galaxy classification, Monthly Notices of the Royal Astronomical Society
Bowles, M., Scaife, A. M., Porter, F., Tang, H., & Bastien, D. J., 2021 · 2021
Cited alongside, same era.
Slijepcevic, I. V. & Scaife, A. M. M., 2021 · 2021
Later among the works it cites.
AstroVaDEr: Astronomical variational deep embedder for unsupervised morphological classification of galaxies and synthetic image generation, Monthly Notices of the Royal Astronomical Society
Spindler, A., Geach, J. E., & Smith, M. J., 2021 · 2021
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Self-supervised similarity search for large scientific datasets, Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021)
Stein, G., Harrington, P., Blaum, J., Medan, T., & Lukic, Z., 2021b · 2021
Later among the works it cites.
ResNet strikes back: An improved training procedure in timm, CoRR
Wightman, R., Touvron, H., & Jégou, H., 2021 · 2021
Later among the works it cites.
Masked Autoencoders Are Scalable Vision Learners, in CVPR
He, K., Chen, X., Xie, S., Li, Y., Dollar, P., & Girshick, R., 2022 · 2022
Later among the works it cites.
MIGHTEE: Total intensity radio continuum imaging and the COSMOS/XMM-LSS Early Science fields, Monthly Notices of the Royal Astronomical Society
Heywood, I., Jarvis, M. J., Hale, C. L., Whittam, I. H., Bester, H. L., Hugo, B., Kenyon, J. S., Prescott, M., Smirnov, O. M., Tasse, C., Afonso, J. M., Best, P. N., Collier, J. D., Deane, R. P., Frank, B. S., Hardcastle, M. J., Knowles, K., Maddox, N., Murphy, E. J., Prandoni, I., Randriamampandry, S. M., Santos, M. G., Sekhar, S., Tabatabaei, F., Taylor, A. R., & Thorat, K., 2022 · 2022
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How well do contrastively trained models transfer?, ICML 2022: The First Workshop on Pre-training
Moein Shariatnia, M., Entezari, R., Wortsman, M., Saukh, O., & Schmidt, L., 2022 · 2022
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Quantifying uncertainty in deep learning approaches to radio galaxy classification, Monthly Notices of the Royal Astronomical Society
Mohan, D., Scaife, A. M. M., Porter, F., Walmsley, M., & Bowles, M., 2022 · 2022
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LaplaceNet: A Hybrid Energy-Neural Model for Deep Semi-Supervised Classification, IEEE Transactions on Neural Networks and Learning Systems
Sellars, P., Aviles-Rivero, A. I., & Schönlieb, C.-B., 2022 · 2022
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How robust are pre-trained models to distribution shift?, ICML 2022: The First Workshop on Pre-training
Shi, Y., Daunhawer, I., Vogt, J. E., Torr, P. H. S., & Sanyal, A., 2022 · 2022
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Learning useful representations for radio astronomy "in the wild" with contrastive learning, ICML 2022 Workshop on Machine Learning for Astrophysics
Slijepcevic, I. V., Scaife, A. M. M., Walmsley, M., & Bowles, M., 2022b · 2022
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Towards Galaxy Foundation Models with Hybrid Contrastive Learning, ICML 2022 Workshop on Machine Learning for Astrophysics
Walmsley, M., Slijepcevic, I. V., Bowles, M., & Scaife, A. M. M., 2022 · 2022
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Is Self-Supervised Learning More Robust Than Supervised Learning?, ICML 2022: The First Workshop on Pre-training
Zhong, Y., Tang, H., Chen, J., Peng, J., & Wang, Y.-X., 2022 · 2022
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MiraBest: A Dataset of Morphologically Classified Radio Galaxies for Machine Learning, RAS Techniques and Instruments
Porter, F. A. M. & Scaife, A. M. M., 2023 · 2023
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