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Found 3 papers, 3 papers with code
Scaling Laws for Galaxy Images
We then compare the downstream performance of finetuned models pretrained on either ImageNet-12k alone vs. additionally pretrained on our galaxy images.
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.
Weight Pruning and Uncertainty in Radio Galaxy Classification
In this work we use variational inference to quantify the degree of epistemic uncertainty in model predictions of radio galaxy classification and show that the level of model posterior variance for individual test samples is correlated with human uncertainty when labelling radio galaxies.
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