Deep Learning for Morphological Identification of Extended Radio Galaxies using Weak Labels. (arXiv:2308.05166v1 [astro-ph.IM])

The present work discusses the use of a weakly-supervised deep learning
algorithm that reduces the cost of labelling pixel-level masks for complex
radio galaxies with multiple components. The algorithm is trained on weak
class-level labels of radio galaxies to get class activation maps (CAMs). The
CAMs are further refined using an inter-pixel relations network (IRNet) to get
instance segmentation masks over radio galaxies and the positions of their
infrared hosts. We use data from the Australian Square Kilometre Array
Pathfinder …

algorithm arxiv astro components cost deep learning identification labelling labels maps masks multiple pixel radio weakly-supervised work