  Deep learning methods can classify various unstructured data such as images,
language, and voice as input data. As the task of classifying anomalies becomes
more important in the real world, various methods exist for classifying using
deep learning with data collected in the real world. As the task of classifying
anomalies becomes more important in the real world, there are various methods
for classifying using deep learning with data collected in the real world.
Among the various methods, the representative approach is a method of
extracting and learning the main features based on a transition model from
pre-trained models, and a method of learning an autoencoderbased structure only
with normal data and classifying it as abnormal through a threshold value.
However, if the dataset is imbalanced, even the state-of-the-arts models do not
achieve good performance. This can be addressed by augmenting normal and
abnormal features in imbalanced data as features with strong distinction. We
use the features of the autoencoder to train latent vectors from low to high
dimensionality. We train normal and abnormal data as a feature that has a
strong distinction among the features of imbalanced data. We propose a latent
vector expansion autoencoder model that improves classification performance at
imbalanced data. The proposed method shows performance improvement compared to
the basic autoencoder using imbalanced anomaly dataset.
