Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning
Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning
We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies. Bayesian CNN can learn from galaxy images with uncertain labels and then, for previously unlabelled galaxies, predict the probability of each possible label. Our posteriors …