DeepMerge – II. Building robust deep learning algorithms for merging galaxy identification across domains
Aleksandra Ćiprijanović(Fermi National Accelerator Laboratory), B. Nord(Fermi National Accelerator Laboratory), Sandeep Madireddy(Argonne National Laboratory), Gabriel Perdue(Fermi National Accelerator Laboratory), Diana Kafkes(Fermi National Accelerator Laboratory), Gregory F. Snyder(Space Telescope Science Institute), Sydney Jenkins(University of Chicago), Travis Johnston(Oak Ridge National Laboratory), Kathryn Downey(University of Chicago)
Cited by 46
Related Papers
Biological underpinnings for lifelong learning machines
|Nature Machine Intelligence|2022|223
DeepMerge: Classifying high-redshift merging galaxies with deep neural networks
|Astronomy and Computing|2020|56
DeepGhostBusters: Using Mask R-CNN to detect and mask ghosting and scattered-light artifacts from optical survey images
|Astronomy and Computing|2022|21
DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification
|Machine Learning Science and Technology|2022|20
DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection
|Machine Learning Science and Technology|2023|17