1 code implementation • 3 Apr 2024 • Mike Walmsley, Micah Bowles, Anna M. M. Scaife, Jason Shingirai Makechemu, Alexander J. Gordon, Annette M. N. Ferguson, Robert G. Mann, James Pearson, Jürgen J. Popp, Jo Bovy, Josh Speagle, Hugh Dickinson, Lucy Fortson, Tobias Géron, Sandor Kruk, Chris J. Lintott, Kameswara Mantha, Devina Mohan, David O'Ryan, Inigo V. Slijepevic
We then compare the downstream performance of finetuned models pretrained on either ImageNet-12k alone vs. additionally pretrained on our galaxy images.
no code implementations • 5 Dec 2023 • Mike Walmsley, Anna M. M. Scaife
We identify rare and visually distinctive galaxy populations by searching for structure within the learned representations of pretrained models.
1 code implementation • 5 Dec 2023 • Mike Walmsley, Ashley Spindler
We present the first deep learning model for segmenting galactic spiral arms and bars.
1 code implementation • 26 Oct 2022 • Micah Bowles, Hongming Tang, Eleni Vardoulaki, Emma L. Alexander, Yan Luo, Lawrence Rudnick, Mike Walmsley, Fiona Porter, Anna M. M. Scaife, Inigo Val Slijepcevic, Gary Segal
We define deriving semantic class targets as a novel multi-modal task.
1 code implementation • 23 Jun 2022 • Mike Walmsley, Inigo Val Slijepcevic, Micah Bowles, Anna M. M. Scaife
New astronomical tasks are often related to earlier tasks for which labels have already been collected.
1 code implementation • 19 Apr 2022 • Inigo V. Slijepcevic, Anna M. M. Scaife, Mike Walmsley, Micah Bowles, Ivy Wong, Stanislav S. Shabala, Hongming Tang
In this work we examine the classification accuracy and robustness of a state-of-the-art semi-supervised learning (SSL) algorithm applied to the morphological classification of radio galaxies.
1 code implementation • 4 Jan 2022 • Devina Mohan, Anna M. M. Scaife, Fiona Porter, Mike Walmsley, Micah Bowles
In this work we use variational inference to quantify the degree of uncertainty in deep learning model predictions of radio galaxy classification.
1 code implementation • 25 Oct 2021 • Mike Walmsley, Anna M. M. Scaife, Chris Lintott, Michelle Lochner, Verlon Etsebeth, Tobias Géron, Hugh Dickinson, Lucy Fortson, Sandor Kruk, Karen L. Masters, Kameswara Bharadwaj Mantha, Brooke D. Simmons
Models fine-tuned from our representation are better able to identify ring galaxies than models fine-tuned from terrestrial images (ImageNet) or trained from scratch.
no code implementations • 30 Apr 2021 • Janet Rafner, Miroslav Gajdacz, Gitte Kragh, Arthur Hjorth, Anna Gander, Blanka Palfi, Aleks Berditchevskaia, François Grey, Kobi Gal, Avi Segal, Mike Walmsley, Josh Aaron Miller, Dominik Dellerman, Muki Haklay, Pietro Michelucci, Jacob Sherson
This "HI lens" provides the CS community with an overview of several ways to utilize the combination of AI and human intelligence in their projects.
1 code implementation • 16 Feb 2021 • Mike Walmsley, Chris Lintott, Tobias Geron, Sandor Kruk, Coleman Krawczyk, Kyle W. Willett, Steven Bamford, Lee S. Kelvin, Lucy Fortson, Yarin Gal, William Keel, Karen L. Masters, Vihang Mehta, Brooke D. Simmons, Rebecca Smethurst, Lewis Smith, Elisabeth M. Baeten, Christine Macmillan
All classifications are used to train an ensemble of Bayesian convolutional neural networks (a state-of-the-art deep learning method) to predict posteriors for the detailed morphology of all 314, 000 galaxies.
1 code implementation • 17 May 2019 • Mike Walmsley, Lewis Smith, Chris Lintott, Yarin Gal, Steven Bamford, Hugh Dickinson, Lucy Fortson, Sandor Kruk, Karen Masters, Claudia Scarlata, Brooke Simmons, Rebecca Smethurst, Darryl Wright
We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies.