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Found 5 papers, 5 papers with code
A New Task: Deriving Semantic Class Targets for the Physical Sciences
We define deriving semantic class targets as a novel multi-modal task.
Quantifying Uncertainty in Deep Learning Approaches to Radio Galaxy Classification
In this work we use variational inference to quantify the degree of uncertainty in deep learning model predictions of radio galaxy classification.
Fanaroff-Riley classification of radio galaxies using group-equivariant convolutional neural networks
Weight sharing in convolutional neural networks (CNNs) ensures that their feature maps will be translation-equivariant.
Instrumentation and Methods for Astrophysics Astrophysics of Galaxies
Structured Variational Inference for Simulating Populations of Radio Galaxies
We present a model for generating postage stamp images of synthetic Fanaroff-Riley Class I and Class II radio galaxies suitable for use in simulations of future radio surveys such as those being developed for the Square Kilometre Array.
Data Augmentation
Variational Inference
Instrumentation and Methods for Astrophysics
Astrophysics of Galaxies
Attention-gating for improved radio galaxy classification
In this work we introduce attention as a state of the art mechanism for classification of radio galaxies using convolutional neural networks.
