Standard classification theory assumes that the distribution of images in the
test and training sets are identical. Unfortunately, real-life scenarios
typically feature unseen data ("out-of-distribution data") which is different
from data in the training distribution("in-distribution"). This issue is most
prevalent in social justice problems where data from under-represented groups
may appear in the test data without representing an equal proportion of the
training data. This may result in a model returning confidently wrong decisions
and predictions. We are interested in the following question: Can the
performance of a neural network improve on facial images of out-of-distribution
data when it is trained simultaneously on multiple datasets of in-distribution
data? We approach this problem by incorporating the Outlier Exposure model and
investigate how the model's performance changes when other datasets of facial
images were implemented. We observe that the accuracy and other metrics of the
model can be increased by applying Outlier Exposure, incorporating a trainable
weight parameter to increase the machine's emphasis on outlier images, and by
re-weighting the importance of different class labels. We also experimented
with whether sorting the images and determining outliers via image features
would have more of an effect on the metrics than sorting by average pixel
value. Our goal was to make models not only more accurate but also more fair by
scanning a more expanded range of images. We also tested the datasets in
reverse order to see whether a more fair dataset with balanced features has an
effect on the model's accuracy.

通过在多个数据集的脸部图像中同时训练，我们研究了异常数据量模型的性能，观察到 Outlier Exposure 方法可以提高模型的准确性和其他度量指标，并通过引入可训练的权重参数以及重新加权不同类别标签的重要性来增强模型的表现。同时，我们还进行了实验，比较了通过图像特征和平均像素值对图像进行排序以及检测异常值的效果，并测试了数据集的逆序是否对模型的准确性产生影响，以期使模型不仅更准确，而且更公平。