Radio Galaxy Zoo：利用变分自编码器的潜在空间表示
Radio Galaxy Zoo: Leveraging latent space representations from variational autoencoder
AI总结：
本研究利用变分自编码器学习射电星系潜在表示，在分类和相似性搜索中取得与深度学习相当的性能，并验证了潜在编码的有效性。
摘要：
我们提出学习射电星系的潜在空间表示，并为此在未标记数据集RGZ DR1上训练一个非常深的变分自编码器（VDVAE）。我们展示了编码特征可用于下游任务，如对标记数据集中的星系进行分类以及相似性搜索。结果表明，该模型能够重建其给定输入，并捕捉后者的显著特征。我们使用来自MiraBest Confident和FR-DEEP NVSS数据集的星系图像的潜在编码，训练各种非神经网络分类器。结果发现，后者能够区分FRI和FRII星系，在MiraBest Confident数据集上实现了准确率≥76%，ROC-AUC≥0.86，特异性≥0.73，召回率≥0.78，与先前研究获得的结果相当。在FR-DEEP NVSS数据表示上训练的简单分类器的性能与先前工作中在图像上训练的深度学习分类器（基于CNN）相当，突显了压缩信息的强大之处。我们成功利用学习到的表示在数据集中搜索与属于不同数据集的查询图像在语义上相似的星系。虽然生成新的星系图像（例如用于数据增强）不是我们的主要目标，但我们发现VDVAE模型是一个相对较好的模拟器。最后，作为检测异常/新颖性的一步，我们在潜在编码上训练了一个密度估计器——掩码自回归流（MAF），以便估计数据的对数似然。本工作中进行的下游任务证明了潜在编码的意义。
英文摘要：
We propose to learn latent space representations of radio galaxies, and train a very deep variational autoencoder (\protect\Verb+VDVAE+) on RGZ DR1, an unlabeled dataset, to this end. We show that the encoded features can be leveraged for downstream tasks such as classifying galaxies in labeled datasets, and similarity search. Results show that the model is able to reconstruct its given inputs, capturing the salient features of the latter. We use the latent codes of galaxy images, from MiraBest Confident and FR-DEEP NVSS datasets, to train various non-neural network classifiers. It is found that the latter can differentiate FRI from FRII galaxies achieving \textit{accuracy} $\ge 76\%$, \textit{roc-auc} $\ge 0.86$, \textit{specificity} $\ge 0.73$ and \textit{recall} $\ge 0.78$ on MiraBest Confident dataset, comparable to results obtained in previous studies. The performance of simple classifiers trained on FR-DEEP NVSS data representations is on par with that of a deep learning classifier (CNN based) trained on images in previous work, highlighting how powerful the compressed information is. We successfully exploit the learned representations to search for galaxies in a dataset that are semantically similar to a query image belonging to a different dataset. Although generating new galaxy images (e.g. for data augmentation) is not our primary objective, we find that the \protect\Verb+VDVAE+ model is a relatively good emulator. Finally, as a step toward detecting anomaly/novelty, a density estimator -- Masked Autoregressive Flow (\protect\Verb+MAF+) -- is trained on the latent codes, such that the log-likelihood of data can be estimated. The downstream tasks conducted in this work demonstrate the meaningfulness of the latent codes.
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