# Unsupervised Model Selection for Variational Disentangled Representation Learning

arXiv: https://arxiv.org/abs/1905.12614 · HTML: https://mlsanity.com/paper/1905.12614/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2019.

## Built on by (most cited first, 30 of 46)

- [Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA](https://mlsanity.com/paper/2107.10098/) (2021)
- [Learning Disentangled Representations in the Imaging Domain](https://mlsanity.com/paper/2108.12043/) (2021)
- [Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning](https://mlsanity.com/paper/2211.14666/) (2022)
- [Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling](https://mlsanity.com/paper/2211.03553/) (2022)
- [Generalizing Nonlinear ICA Beyond Structural Sparsity](https://mlsanity.com/paper/2311.00866/) (2023)
- [Partial Disentanglement via Mechanism Sparsity](https://mlsanity.com/paper/2207.07732/) (2022)
- [Where and What? Examining Interpretable Disentangled Representations](https://mlsanity.com/paper/2104.05622/) (2021)
- [Efficient-VDVAE: Less is more](https://mlsanity.com/paper/2203.13751/) (2022)
- [Unsupervised Model Selection for Time-series Anomaly Detection](https://mlsanity.com/paper/2210.01078/) (2022)
- [Toward Unsupervised Outlier Model Selection](https://mlsanity.com/paper/2211.01834/) (2022)
- [3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces](https://mlsanity.com/paper/2111.12448/) (2021)
- [How Diffusion Models Learn to Factorize and Compose](https://mlsanity.com/paper/2408.13256/) (2024)
- [Lost in Latent Space: Disentangled Models and the Challenge of Combinatorial Generalisation](https://mlsanity.com/paper/2204.02283/) (2022)
- [How do Variational Autoencoders Learn? Insights from Representational Similarity](https://mlsanity.com/paper/2205.08399/) (2022)
- [Review of Disentanglement Approaches for Medical Applications -- Towards Solving the Gordian Knot of Generative Models in Healthcare](https://mlsanity.com/paper/2203.11132/) (2022)
- [Unsupervised Anomaly Detection with Rejection](https://mlsanity.com/paper/2305.13189/) (2023)
- [Identifying Interpretable Visual Features in Artificial and Biological Neural Systems](https://mlsanity.com/paper/2310.11431/) (2023)
- [Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research](https://mlsanity.com/paper/2504.13101/) (2025)
- [A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?](https://mlsanity.com/paper/2104.01422/) (2021)
- [Graph Anomaly Detection with Unsupervised GNNs](https://mlsanity.com/paper/2210.09535/) (2022)
- [DAVA: Disentangling Adversarial Variational Autoencoder](https://mlsanity.com/paper/2303.01384/) (2023)
- [Fast Unsupervised Deep Outlier Model Selection with Hypernetworks](https://mlsanity.com/paper/2307.10529/) (2023)
- [Demystifying Inductive Biases for β-VAE Based Architectures](https://mlsanity.com/paper/2102.06822/) (2021)
- [SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision](https://mlsanity.com/paper/2111.05986/) (2021)
- [Which Student is Best? A Comprehensive Knowledge Distillation Exam for Task-Specific BERT Models](https://mlsanity.com/paper/2201.00558/) (2022)
- [Representational Similarity via Interpretable Visual Concepts](https://mlsanity.com/paper/2503.15699/) (2025)
- [Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning](https://mlsanity.com/paper/2112.08830/) (2021)
- [A Category-theoretical Meta-analysis of Definitions of Disentanglement](https://mlsanity.com/paper/2305.06886/) (2023)
- [Disentangled Representation Learning for Parametric Partial Differential Equations](https://mlsanity.com/paper/2410.02136/) (2024)
- [Unsupervised Decomposition and Recombination with Discriminator-Driven Diffusion Models](https://mlsanity.com/paper/2601.22057/) (2026)

## Abstract (arXiv)

Disentangled representations have recently been shown to improve fairness, data efficiency and generalisation in simple supervised and reinforcement learning tasks. To extend the benefits of disentangled representations to more complex domains and practical applications, it is important to enable hyperparameter tuning and model selection of existing unsupervised approaches without requiring access to ground truth attribute labels, which are not available for most datasets. This paper addresses this problem by introducing a simple yet robust and reliable method for unsupervised disentangled model selection. Our approach, Unsupervised Disentanglement Ranking (UDR), leverages the recent theoretical results that explain why variational autoencoders disentangle (Rolinek et al, 2019), to quantify the quality of disentanglement by performing pairwise comparisons between trained model representations. We show that our approach performs comparably to the existing supervised alternatives across 5,400 models from six state of the art unsupervised disentangled representation learning model classes. Furthermore, we show that the ranking produced by our approach correlates well with the final task performance on two different domains.