Measuring the Biases and Effectiveness of Content-Style Disentanglement

TL;DR

This paper empirically investigates how different biases and design choices affect content-style disentanglement in images, revealing a balance point where disentanglement, performance, and interpretability are optimized.

Contribution

It provides a systematic analysis of biases and constraints in content-style disentanglement models, highlighting the optimal trade-offs for improved task performance.

Findings

Higher disentanglement can reduce task performance.

There is a 'sweet spot' balancing disentanglement and content interpretability.

Relaxing constraints can improve or harm model effectiveness depending on the setting.

Abstract

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.