Latent Representations of Terrain in Aerial Image Classification
Pylyp Prystavka, Serge Dolgikh, Olha Cholyshkina, Oleksandr Kozachuk · 2021
Investigation of informative representations of complex data is a rapidly developing field of research in machine learning. In this work we present a process of production and analysis of informative low-dimensional latent representations of real-world image data with neural network models of unsupervised generative learning. A model of convolutional autoencoder based on VGG-16 architecture was used to produce low-dimensional latent representations of aerial image data and the characteristics of distributions of several higher-level classes of terrain types were studied. The analysis of distributions demonstrated a landscape of compact concept clusters for most studied types of terrain with good separation between concept regions. The results of this work can be used in developing methods of effective learning with minimal labeled data based on the emergent concept-sensitive structure in the latent representations.