# Measuring the Biases and Effectiveness of Content-Style Disentanglement

arXiv: https://arxiv.org/abs/2008.12378 · HTML: https://mlsanity.com/paper/2008.12378/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2020.

## Built on by (most cited first, 14 of 14)

- [Learning Disentangled Representations in the Imaging Domain](https://mlsanity.com/paper/2108.12043/) (2021)
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- [Not Only Generative Art: Stable Diffusion for Content-Style Disentanglement in Art Analysis](https://mlsanity.com/paper/2304.10278/) (2023)
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- [Disentangled and Interpretable Multimodal Attention Fusion for Cancer Survival Prediction](https://mlsanity.com/paper/2503.16069/) (2025)
- [Supervised Attribute Information Removal and Reconstruction for Image Manipulation](https://mlsanity.com/paper/2207.06555/) (2022)
- [Towards General Game Representations: Decomposing Games Pixels into Content and Style](https://mlsanity.com/paper/2307.11141/) (2023)
- [Compositionally Equivariant Representation Learning](https://mlsanity.com/paper/2306.07783/) (2023)
- [Understanding Pose and Appearance Disentanglement in 3D Human Pose Estimation](https://mlsanity.com/paper/2309.11667/) (2023)
- [Benchmarking Dependence Measures to Prevent Shortcut Learning in Medical Imaging](https://mlsanity.com/paper/2407.18792/) (2024)
- [Bridging the gap between Performance and Interpretability: An Explainable Disentangled Multimodal Framework for Cancer Survival Prediction](https://mlsanity.com/paper/2603.02162/) (2026)

## Abstract (arXiv)

A recent spate of state-of-the-art semi- and un-supervised solutions disentangle and encode image "content" into a spatial tensor and image appearance or "style" into a vector, to achieve good performance in spatially equivariant tasks (e.g. image-to-image translation). To achieve this, they employ different model design, learning objective, and data biases. While considerable effort has been made to measure disentanglement in vector representations, and assess its impact on task performance, such analysis for (spatial) content - style disentanglement is lacking. In this paper, we conduct an empirical study to investigate the role of different biases in content-style disentanglement settings and unveil the relationship between the degree of disentanglement and task performance. In particular, we consider the setting where we: (i) identify key design choices and learning constraints for three popular content-style disentanglement models; (ii) relax or remove such constraints in an ablation fashion; and (iii) use two metrics to measure the degree of disentanglement and assess its effect on each task performance. Our experiments reveal that there is a "sweet spot" between disentanglement, task performance and - surprisingly - content interpretability, suggesting that blindly forcing for higher disentanglement can hurt model performance and content factors semanticness. Our findings, as well as the used task-independent metrics, can be used to guide the design and selection of new models for tasks where content-style representations are useful.