Self-supervised autoencoders for clustering and classification

Clustering techniques aim at finding meaningful groups of data samples which exhibit similarity with regards to a set of characteristics, typically measured in terms of pairwise distances. Due to the so-called curse of dimensionality, i.e., the observation that high-dimensional spaces are unsuited for measuring distances, distance-based clustering techniques such as the classic k-means algorithm fail to uncover meaningful clusters in high-dimensional spaces. Thus, dimensionality reduction techniques can be used to greatly improve the performance of such clustering methods. In this work, we study Autoencoders as Deep Learning tools for dimensionality reduction, and combine them with k-means clustering to learn low-dimensional representations which improve the clustering performance by enhancing intra-cluster relationships and suppressing inter-cluster ones, in a self-supervised manner. In the supervised paradigm, distance-based classifiers may also greatly benefit from robust dimensionality reduction techniques. The proposed method is evaluated via multiple experiments on datasets of handwritten digits, various objects and faces, and is shown to improve external cluster quality measuring criteria. A fully supervised counterpart is also evaluated on two face recognition datasets, and is shown to improve the performance of various lightweight classifiers, allowing their use in real-time applications on devices with limited computational resources, such as Unmanned Aerial Vehicles (UAVs).