This is an alpha release currently undergoing development. Examples and documentation will be added upon release of the accompanying publication. Not all features have been validated and may change without notice. Use at your own risk.
Self-SNE is a probabilistic family of self-supervised deep learning models for compressing high-dimensional data to a low-dimensional embedding. It is a general-purpose modelling framework for multiple types of data including images, sequences, and tabular data. It uses self-supervised objectives to preserve structure in the compressed latent space.
Released under a Apache 2.0 License. See LICENSE for details.