5 citations · 10 across the 3 of their papers we have counts for
4 papers
Robustness of deep learning algorithms in astronomy -- galaxy morphology studies
A. Ćiprijanović, D. Kafkes, G. N. Perdue +6
Deep learning models are being increasingly adopted in wide array of scientific domains, especially to handle high-dimensionality and volume of the scientific data. However, these…
DeepGhostBusters: Using Mask R-CNN to Detect and Mask Ghosting and Scattered-Light Artifacts from Optical Survey Images
Dimitrios Tanoglidis, Aleksandra Ćiprijanović, Alex Drlica-Wagner +7
Wide-field astronomical surveys are often affected by the presence of undesirable reflections (often known as "ghosting artifacts" or "ghosts") and scattered-light artifacts. The i…
DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains
A. Ćiprijanović, D. Kafkes, K. Downey +6
In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. Unfortunately, training a model on simulation data and…
Domain adaptation techniques for improved cross-domain study of galaxy mergers
A. Ćiprijanović, D. Kafkes, S. Jenkins +5
In astronomy, neural networks are often trained on simulated data with the prospect of being applied to real observations. Unfortunately, simply training a deep neural network on i…