Artificial intelligence methods show great promise in increasing the quality
and speed of work with large astronomical datasets, but the high complexity of
these methods leads to the extraction of dataset-specific, non-robust features.
Therefore, such methods do not generalize well across multiple datasets. We
present a universal domain adaptation method, \textit{DeepAstroUDA}, as an
approach to overcome this challenge. This algorithm performs semi-supervised
domain adaptation and can be applied to datasets with different data
distributions and class overlaps. Non-overlapping classes can be present in any
of the two datasets (the labeled source domain, or the unlabeled target
domain), and the method can even be used in the presence of unknown classes. We
apply our method to three examples of galaxy morphology classification tasks of
different complexities ($3$-class and $10$-class problems), with anomaly
detection: 1) datasets created after different numbers of observing years from
a single survey (LSST mock data of $1$ and $10$ years of observations); 2) data
from different surveys (SDSS and DECaLS); and 3) data from observing fields
with different depths within one survey (wide field and Stripe 82 deep field of
SDSS). For the first time, we demonstrate the successful use of domain
adaptation between very discrepant observational datasets.
\textit{DeepAstroUDA} is capable of bridging the gap between two astronomical
surveys, increasing classification accuracy in both domains (up to $40\%$ on
the unlabeled data), and making model performance consistent across datasets.
Furthermore, our method also performs well as an anomaly detection algorithm
and successfully clusters unknown class samples even in the unlabeled target
dataset.

本文介绍了一种名为 DeepAstroUDA 的通用领域自适应方法，可用于具有不同数据分布和类别重叠的数据集，以及存在未知类的情况。我们将其应用于三个不同复杂度的星系形态分类任务的示例中，成功使用领域自适应方法实现了极度不一致的观测数据的分类，提高了模型性能，使模型能够在多个数据集上实现一致的表现。同时，我们的方法也用作了一种异常检测算法，并可以成功地聚类未知类样本。

DeepAstroUDA: 跨数据收集半监督通用领域自适应，用于星系形态分类和异常检测

DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection

Measuring algorithmic bias is crucial both to assess algorithmic fairness,
and to guide the improvement of algorithms. Current methods to measure
algorithmic bias in computer vision, which are based on observational datasets,
are inadequate for this task because they conflate algorithmic bias with
dataset bias.
To address this problem we develop an experimental method for measuring
algorithmic bias of face analysis algorithms, which manipulates directly the
attributes of interest, e.g., gender and skin tone, in order to reveal causal
links between attribute variation and performance change. Our proposed method
is based on generating synthetic ``transects'' of matched sample images that
are designed to differ along specific attributes while leaving other attributes
constant. A crucial aspect of our approach is relying on the perception of
human observers, both to guide manipulations, and to measure algorithmic bias.
Besides allowing the measurement of algorithmic bias, synthetic transects
have other advantages with respect to observational datasets: they sample
attributes more evenly allowing for more straightforward bias analysis on
minority and intersectional groups, they enable prediction of bias in new
scenarios, they greatly reduce ethical and legal challenges, and they are
economical and fast to obtain, helping make bias testing affordable and widely
available.
We validate our method by comparing it to a study that employs the
traditional observational method for analyzing bias in gender classification
algorithms. The two methods reach different conclusions. While the
observational method reports gender and skin color biases, the experimental
method reveals biases due to gender, hair length, age, and facial hair.

我们提出了一种实验方法来测量面部分析算法的算法偏差，该方法可以操作感兴趣的属性（如性别和肤色），以揭示属性变化与性能变化之间的因果联系，并且相较于观察性数据集，合成横断面具有更均衡的属性采样，可预测新场景中的偏差，在减少道德和法律难题的同时，还可以节省成本，并使偏差测试变得更加负担得起和普遍可用。