  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.
