DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection
Aleksandra Ćiprijanović(Fermi National Accelerator Laboratory), Stefan M. Wild(Argonne National Laboratory), B. Nord(Fermi National Accelerator Laboratory), Sandeep Madireddy(Argonne National Laboratory), Gabriel Perdue(Fermi National Accelerator Laboratory), Ashia Lewis(Fermi National Accelerator Laboratory), K. Pedro(Fermi National Accelerator Laboratory)
Cited by 17
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
DeepMerge – II. Building robust deep learning algorithms for merging galaxy identification across domains
|Monthly Notices of the Royal Astronomical Society|2021|46
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