# GLSR-VAE: Geodesic Latent Space Regularization for Variational AutoEncoder Architectures

arXiv: https://arxiv.org/abs/1707.04588 · HTML: https://mlsanity.com/paper/1707.04588/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2017.

## Built on by (most cited first, 22 of 22)

- [Consistency Regularization for Variational Auto-Encoders](https://mlsanity.com/paper/2105.14859/) (2021)
- [MuseMorphose: Full-Song and Fine-Grained Piano Music Style Transfer with One Transformer VAE](https://mlsanity.com/paper/2105.04090/) (2021)
- [The Piano Inpainting Application](https://mlsanity.com/paper/2107.05944/) (2021)
- [Isometric Representation Learning for Disentangled Latent Space of Diffusion Models](https://mlsanity.com/paper/2407.11451/) (2024)
- [Factor Analysis, Probabilistic Principal Component Analysis, Variational Inference, and Variational Autoencoder: Tutorial and Survey](https://mlsanity.com/paper/2101.00734/) (2021)
- [Attri-VAE: attribute-based interpretable representations of medical images with variational autoencoders](https://mlsanity.com/paper/2203.10417/) (2022)
- [Hierarchically Structured Task-Agnostic Continual Learning](https://mlsanity.com/paper/2211.07725/) (2022)
- [Exploring XAI for the Arts: Explaining Latent Space in Generative Music](https://mlsanity.com/paper/2308.05496/) (2023)
- [Is Disentanglement enough? On Latent Representations for Controllable Music Generation](https://mlsanity.com/paper/2108.01450/) (2021)
- [A Detailed Study of Interpretability of Deep Neural Network based Top Taggers](https://mlsanity.com/paper/2210.04371/) (2022)
- [Exploring Variational Auto-Encoder Architectures, Configurations, and Datasets for Generative Music Explainable AI](https://mlsanity.com/paper/2311.08336/) (2023)
- [Learning Latent Graph Structures and their Uncertainty](https://mlsanity.com/paper/2405.19933/) (2024)
- [MusER: Musical Element-Based Regularization for Generating Symbolic Music with Emotion](https://mlsanity.com/paper/2312.10307/) (2023)
- [Multi-view MidiVAE: Fusing Track- and Bar-view Representations for Long Multi-track Symbolic Music Generation](https://mlsanity.com/paper/2401.07532/) (2024)
- [A Survey of Music Generation in the Context of Interaction](https://mlsanity.com/paper/2402.15294/) (2024)
- [Variational Rank Reduction Autoencoders](https://mlsanity.com/paper/2505.09458/) (2025)
- [Flat Latent Manifolds for Human-machine Co-creation of Music](https://mlsanity.com/paper/2202.12243/) (2022)
- [A General Framework for Learning Prosodic-Enhanced Representation of Rap Lyrics](https://mlsanity.com/paper/2103.12615/) (2021)
- [Attribute Regularized Soft Introspective VAE: Towards Cardiac Attribute Regularization Through MRI Domains](https://mlsanity.com/paper/2307.12618/) (2023)
- [Attribute Regularized Soft Introspective Variational Autoencoder for Interpretable Cardiac Disease Classification](https://mlsanity.com/paper/2312.08915/) (2023)
- [On the Joint Minimization of Regularization Loss Functions in Deep Variational Bayesian Methods for Attribute-Controlled Symbolic Music Generation](https://mlsanity.com/paper/2511.07118/) (2025)
- [Twincher: Bijective Representation Learning for Robust Inversion of Continuous Systems](https://mlsanity.com/paper/2605.13470/) (2026)

## Abstract (arXiv)

VAEs (Variational AutoEncoders) have proved to be powerful in the context of density modeling and have been used in a variety of contexts for creative purposes. In many settings, the data we model possesses continuous attributes that we would like to take into account at generation time. We propose in this paper GLSR-VAE, a Geodesic Latent Space Regularization for the Variational AutoEncoder architecture and its generalizations which allows a fine control on the embedding of the data into the latent space. When augmenting the VAE loss with this regularization, changes in the learned latent space reflects changes of the attributes of the data. This deeper understanding of the VAE latent space structure offers the possibility to modulate the attributes of the generated data in a continuous way. We demonstrate its efficiency on a monophonic music generation task where we manage to generate variations of discrete sequences in an intended and playful way.