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Found 5 papers, 3 papers with code
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
This algorithm performs semi-supervised domain adaptation and can be applied to datasets with different data distributions and class overlaps.
Robustness of deep learning algorithms in astronomy -- galaxy morphology studies
Deep learning models are being increasingly adopted in wide array of scientific domains, especially to handle high-dimensionality and volume of the scientific data.
DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains
Here we employ domain adaptation techniques$-$ Maximum Mean Discrepancy (MMD) as an additional transfer loss and Domain Adversarial Neural Networks (DANNs)$-$ and demonstrate their viability to extract domain-invariant features within the astronomical context of classifying merging and non-merging galaxies.
Domain adaptation techniques for improved cross-domain study of galaxy mergers
In astronomy, neural networks are often trained on simulated data with the prospect of being applied to real observations.
DeepMerge: Classifying High-redshift Merging Galaxies with Deep Neural Networks
The test set classification accuracy of the CNN is $79\%$ for pristine and $76\%$ for noisy.
