Multi-Domain Outlier Detection Dataset

The Multi-Domain Outlier Detection Dataset contains datasets for conducting outlier detection experiments for four different application domains: Astrophysics - detecting anomalous observations in the Dark Energy Survey (DES) catalog (data type: feature vectors) Planetary science - selecting novel geologic targets for follow-up observation onboard the Mars Science Laboratory (MSL) rover (data type: grayscale images) Earth science: detecting anomalous samples in satellite time series corresponding to ground-truth observations of maize crops (data type: time series/feature vectors) Fashion-MNIST/MNIST: benchmark task to detect anomalous MNIST images among Fashion-MNIST images (data type: grayscale images) Each dataset contains a "fit" dataset (used for fitting or training outlier detection models), a "score" dataset (used for scoring samples used to evaluate model performance, analogous to test set), and a label dataset (indicates whether samples in the score dataset are considered outliers or not in the domain of each dataset). To read more about the datasets and how they are used for outlier detection, or to cite this dataset in your own work, please see the following citation: Kerner, H. R., Rebbapragada, U., Wagstaff, K. L., Lu, S., Dubayah, B., Huff, E., Lee, J., Raman, V., and Kulshrestha, S. (2022). Domain-agnostic Outlier Ranking Algorithms (DORA)-A Configurable Pipeline for Facilitating Outlier Detection in Scientific Datasets. Under review for <em>Frontiers in Astronomy and Space Sciences</em>.

多域异常检测数据集（Multi-Domain Outlier Detection Dataset）包含面向四大应用领域开展异常检测实验的数据集：天体物理学领域——针对暗能量巡天（Dark Energy Survey, DES）星表中的异常观测开展检测，数据类型为特征向量；行星科学领域——为火星科学实验室（Mars Science Laboratory, MSL）漫游车的机载后续观测遴选新型地质目标，数据类型为灰度图像；地球科学领域——在对应玉米作物实地观测真值的卫星时间序列数据中检测异常样本，数据类型为时间序列/特征向量；Fashion-MNIST/MNIST基准任务：在Fashion-MNIST图像集中检测MNIST异常图像，数据类型为灰度图像。所有数据集均包含三类数据文件："fit"数据集（用于拟合或训练异常检测模型）、"score"数据集（用于对样本进行评分以评估模型性能，类比测试集）以及标签数据集（用于标注评分数据集中的样本是否属于该领域下的异常样本）。若欲了解该数据集的更多细节、其在异常检测任务中的使用方式，或在您的研究中引用此数据集，请参阅以下引用信息：Kerner, H. R., Rebbapragada, U., Wagstaff, K. L., Lu, S., Dubayah, B., Huff, E., Lee, J., Raman, V., and Kulshrestha, S. (2022). 域无关异常排序算法（Domain-agnostic Outlier Ranking Algorithms, DORA）——面向科学数据集异常检测的可配置流水线。已提交至《天文学与空间科学前沿》（Frontiers in Astronomy and Space Sciences）审阅。