Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning-猫眼课题宝

DOI：

10.1093/mnras/stz2816

发表时间：

2019-05

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Mike Walmsley;Lewis Smith;C. Lintott;Y. Gal;S. Bamford;H. Dickinson;L. Fortson;S. Kruk;K. Masters;C. Scarlata;B. Simmons;R. Smethurst;D. Wright

我们使用贝叶斯卷积神经网络和银河动物园志愿者反应的新生成模型来推断星系视觉形态的后验。贝叶斯CNN可以从具有不确定标签的星系图像中学习，然后对于以前未标记的星系，预测每个可能标签的概率。我们的后验是经过良好校准的（例如，对于预测酒吧，我们在0.2的投票分数偏差内实现了11.8%的覆盖率误差），因此对于实际使用是可靠的。此外，使用我们的后验，我们应用主动学习策略BALD，要求志愿者对星系的子集的反应，如果标记，将是最翔实的训练我们的网络。我们表明，使用主动学习训练贝叶斯CNN需要少35- 60%的标记星系，这取决于被分类的形态特征。通过结合人类和机器智能，银河动物园将能够在数周的时间尺度上对任何可能规模的调查进行分类，提供大量详细的形态目录，以支持对星系演化的研究。

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 are well-calibrated (e.g. for predicting bars, we achieve coverage errors of 11.8 per cent within a vote fraction deviation of 0.2) and hence are reliable for practical use. Further, using our posteriors, we apply the active learning strategy BALD to request volunteer responses for the subset of galaxies which, if labelled, would be most informative for training our network. We show that training our Bayesian CNNs using active learning requires up to 35–60 per cent fewer labelled galaxies, depending on the morphological feature being classified. By combining human and machine intelligence, Galaxy zoo will be able to classify surveys of any conceivable scale on a time-scale of weeks, providing massive and detailed morphology catalogues to support research into galaxy evolution.

Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning

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