47 citations · 58 across the 5 of their papers we have counts for
5 papers
DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification
Aleksandra Ćiprijanović, Diana Kafkes, Gregory Snyder +6
With increased adoption of supervised deep learning methods for processing and analysis of cosmological survey data, the assessment of data perturbation effects (that can naturally…
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…