Exploring Slow Feature Analysis for Extracting Generative Latent Factors

Max Menne, Merlin Schüler, Laurenz Wiskott

2021

Abstract

In this work, we explore generative models based on temporally coherent representations. For this, we incorporate Slow Feature Analysis (SFA) into the encoder of a typical autoencoder architecture. We show that the latent factors extracted by SFA, while allowing for meaningful reconstruction, also result in a well-structured, continuous and complete latent space – favorable properties for generative tasks. To complete the generative model for single samples, we demonstrate the construction of suitable prior distributions based on inherent characteristics of slow features. The efficacy of this method is illustrated on a variant of the Moving MNIST dataset with increased number of generation parameters. By the use of a forecasting model in latent space, we find that the learned representations are also suitable for the generation of image sequences.

Paper Citation

in Bibtex Style

@conference{icpram21,
author={Max Menne and Merlin Schüler and Laurenz Wiskott},
title={Exploring Slow Feature Analysis for Extracting Generative Latent Factors},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2021},
pages={120-131},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010391401200131},
isbn={978-989-758-486-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Exploring Slow Feature Analysis for Extracting Generative Latent Factors
SN - 978-989-758-486-2
AU - Menne M.
AU - Schüler M.
AU - Wiskott L.
PY - 2021
SP - 120
EP - 131
DO - 10.5220/0010391401200131

in Harvard Style

Menne M., Schüler M. and Wiskott L. (2021). Exploring Slow Feature Analysis for Extracting Generative Latent Factors.In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-486-2, pages 120-131. DOI: 10.5220/0010391401200131