# PRI-VAE: Principle-of-Relevant-Information Variational Autoencoders

arXiv: https://arxiv.org/abs/2007.06503 · HTML: https://mlsanity.com/paper/2007.06503/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Principle of Relevant Information for Graph Sparsification](https://mlsanity.com/paper/2206.00118/) (2022)
- [FairDRL-ST: Disentangled Representation Learning for Fair Spatio-Temporal Mobility Prediction](https://mlsanity.com/paper/2508.07518/) (2025)
- [Measuring disentangled generative spatio-temporal representation](https://mlsanity.com/paper/2202.04821/) (2022)
- [Information-Theoretic Hashing for Zero-Shot Cross-Modal Retrieval](https://mlsanity.com/paper/2209.12491/) (2022)

## Abstract (arXiv)

Although substantial efforts have been made to learn disentangled representations under the variational autoencoder (VAE) framework, the fundamental properties to the dynamics of learning of most VAE models still remain unknown and under-investigated. In this work, we first propose a novel learning objective, termed the principle-of-relevant-information variational autoencoder (PRI-VAE), to learn disentangled representations. We then present an information-theoretic perspective to analyze existing VAE models by inspecting the evolution of some critical information-theoretic quantities across training epochs. Our observations unveil some fundamental properties associated with VAEs. Empirical results also demonstrate the effectiveness of PRI-VAE on four benchmark data sets.