Using convolutional neural network autoencoders to understand unlabeled data
Samuel N. Edwards, Michael S. Lee · 2019
Gaining insight from unlabeled data is a widespread, challenging problem with many immediate applications. Clustering, dimensionality reduction for visualization, and anomaly detection are unsupervised learning solutions to this problem. Typically, the efficacy of these methods relies on obtaining sufficient amounts of data, presenting many challenges to cases where only limited data exists. In this work, we demonstrate that deep convolutional autoencoders can comfortably perform these tasks either directly or through manipulations of the latent space in a limited data setting. Clustering on common benchmark datasets produces comparable results to the current state-of-the-art in unsupervised classification. Clustering the activation maps of the encoding layers results in a form of unsupervised image segmentation with limited two-dimensional data. Visualizing the activation maps through dimension reduction demonstrates the possibilities of anomaly detection and semi-supervised learning. We are currently utilizing our versatile autoencoder to explore the ambitious task of finding anomalous and/or inconspicuous objects from single images.