Time domain astronomy is advancing towards the analysis of multiple massive
datasets in real time, prompting the development of multi-stream machine
learning models. In this work, we study Domain Adaptation (DA) for real/bogus
classification of astronomical alerts using four different datasets: HiTS, DES,
ATLAS, and ZTF. We study the domain shift between these datasets, and improve a
naive deep learning classification model by using a fine tuning approach and
semi-supervised deep DA via Minimax Entropy (MME). We compare the balanced
accuracy of these models for different source-target scenarios. We find that
both the fine tuning and MME models improve significantly the base model with
as few as one labeled item per class coming from the target dataset, but that
the MME does not compromise its performance on the source dataset.

该研究在时域天文学中使用多个大规模数据集实时进行分析，开发了多源流的机器学习模型。研究者通过研究 HiTS、DES、ATLAS 和 ZTF 等四个不同数据集之间的领域转换，通过精细调整和半监督深度领域自适应的 Minimax Entropy (MME) 方法改进了一种纯粹深度学习分类模型的性能。通过对不同源目标场景的模型进行平衡准确性比较，发现即便仅使用目标数据集中每类一个标记样本，精细调整和 MME 模型都能显著提高基础模型的性能，而 MME 模型不会影响源数据集上的性能。

基于极小极大熵的领域自适应方法用于天文警报的真伪分类

Domain Adaptation via Minimax Entropy for Real/Bogus Classification of  Astronomical Alerts

Contemporary domain adaptation methods are very effective at aligning feature
distributions of source and target domains without any target supervision.
However, we show that these techniques perform poorly when even a few labeled
examples are available in the target. To address this semi-supervised domain
adaptation (SSDA) setting, we propose a novel Minimax Entropy (MME) approach
that adversarially optimizes an adaptive few-shot model. Our base model
consists of a feature encoding network, followed by a classification layer that
computes the features' similarity to estimated prototypes (representatives of
each class). Adaptation is achieved by alternately maximizing the conditional
entropy of unlabeled target data with respect to the classifier and minimizing
it with respect to the feature encoder. We empirically demonstrate the
superiority of our method over many baselines, including conventional feature
alignment and few-shot methods, setting a new state of the art for SSDA.

提出了一种基于 Minimax Entropy 的半监督域自适应方法，在有限的标记信息下，与其他基线方法相比，提高了无标记目标数据分类的准确性。