  In this paper, we focus on the problem of identifying semantic factors of
variation in large image datasets. By training a convolutional Autoencoder on
the image data, we create encodings, which describe each datapoint at a higher
level of abstraction than pixel-space. We then apply Principal Component
Analysis to the encodings to disentangle the factors of variation in the data.
Sorting the dataset according to the values of individual principal components,
we find that samples at the high and low ends of the distribution often share
specific semantic characteristics. We refer to these groups of samples as
semantic groups. When applied to real-world data, this method can help discover
unwanted edge-cases.
