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Disentangled Representation Learning

Xin Wang, Hong Chen, Si'ao Tang, Zihao Wu, Wenwu Zhu

TL;DR

This survey analyzes Disentangled Representation Learning (DRL) as a framework to separate latent generative factors into independent, semantically meaningful components. It surveys definitions (intuitive and group-theoretic), taxonomy across model types (VAEs, GANs, diffusion, pretrained latent directions), representation structures (dimension-wise vs vector-wise; flat vs hierarchical), supervision schemes (unsupervised, supervised, weakly supervised), and independence vs causality. It also covers evaluation metrics (supervised and unsupervised), broad applications (image, video, NLP, multimodal, graphs, recommendation, few-shot, OOD), and practical DRL design principles (structure, losses) along with future directions such as theory development, improved benchmarks, foundation-model disentanglement, and ethical considerations. The work highlights causal disentanglement, connections to capsule/object-centric learning, and the potential of diffusion and pretrained-model directions to advance DRL in real-world tasks. Overall, DRL provides a principled path to controllable, robust, and generalizable representations with wide-ranging impact across AI domains.

Abstract

Disentangled Representation Learning (DRL) aims to learn a model capable of identifying and disentangling the underlying factors hidden in the observable data in representation form. The process of separating underlying factors of variation into variables with semantic meaning benefits in learning explainable representations of data, which imitates the meaningful understanding process of humans when observing an object or relation. As a general learning strategy, DRL has demonstrated its power in improving the model explainability, controlability, robustness, as well as generalization capacity in a wide range of scenarios such as computer vision, natural language processing, and data mining. In this article, we comprehensively investigate DRL from various aspects including motivations, definitions, methodologies, evaluations, applications, and model designs. We first present two well-recognized definitions, i.e., Intuitive Definition and Group Theory Definition for disentangled representation learning. We further categorize the methodologies for DRL into four groups from the following perspectives, the model type, representation structure, supervision signal, and independence assumption. We also analyze principles to design different DRL models that may benefit different tasks in practical applications. Finally, we point out challenges in DRL as well as potential research directions deserving future investigations. We believe this work may provide insights for promoting the DRL research in the community.

Disentangled Representation Learning

TL;DR

This survey analyzes Disentangled Representation Learning (DRL) as a framework to separate latent generative factors into independent, semantically meaningful components. It surveys definitions (intuitive and group-theoretic), taxonomy across model types (VAEs, GANs, diffusion, pretrained latent directions), representation structures (dimension-wise vs vector-wise; flat vs hierarchical), supervision schemes (unsupervised, supervised, weakly supervised), and independence vs causality. It also covers evaluation metrics (supervised and unsupervised), broad applications (image, video, NLP, multimodal, graphs, recommendation, few-shot, OOD), and practical DRL design principles (structure, losses) along with future directions such as theory development, improved benchmarks, foundation-model disentanglement, and ethical considerations. The work highlights causal disentanglement, connections to capsule/object-centric learning, and the potential of diffusion and pretrained-model directions to advance DRL in real-world tasks. Overall, DRL provides a principled path to controllable, robust, and generalizable representations with wide-ranging impact across AI domains.

Abstract

Disentangled Representation Learning (DRL) aims to learn a model capable of identifying and disentangling the underlying factors hidden in the observable data in representation form. The process of separating underlying factors of variation into variables with semantic meaning benefits in learning explainable representations of data, which imitates the meaningful understanding process of humans when observing an object or relation. As a general learning strategy, DRL has demonstrated its power in improving the model explainability, controlability, robustness, as well as generalization capacity in a wide range of scenarios such as computer vision, natural language processing, and data mining. In this article, we comprehensively investigate DRL from various aspects including motivations, definitions, methodologies, evaluations, applications, and model designs. We first present two well-recognized definitions, i.e., Intuitive Definition and Group Theory Definition for disentangled representation learning. We further categorize the methodologies for DRL into four groups from the following perspectives, the model type, representation structure, supervision signal, and independence assumption. We also analyze principles to design different DRL models that may benefit different tasks in practical applications. Finally, we point out challenges in DRL as well as potential research directions deserving future investigations. We believe this work may provide insights for promoting the DRL research in the community.
Paper Structure (52 sections, 53 equations, 12 figures, 4 tables)

This paper contains 52 sections, 53 equations, 12 figures, 4 tables.

Table of Contents

  1. Introduction
  2. DRL Definitions
  3. DRL Taxonomy
  4. Model Type
  5. VAE-based DRL Methods
  6. GAN-based DRL Methods
  7. Diffusion-based DRL Methods
  8. Disentangled Latent Directions in Pretrained Generative Models
  9. Comparison
  10. Representation Structure
  11. Dimension-wise DRL v.s. Vector-wise DRL
  12. Flat DRL vs. Hierarchical DRL
  13. Supervision Signal
  14. Unsupervised Methods
  15. Supervised Methods
  16. ...and 37 more sections

Figures (12)

  • Figure 1: The scene of Shape3D 3dshapes18, where the six rectangles in the gray circle represent the six factors of variation in the Shape3D respectively. DRL is expected to encode these distinct factors with independent latent variables in the latent feature space.
  • Figure 2: The illustration of condition (ii).
  • Figure 3: Swinging pendulum, light and shadow, figure from Yang_2021_CVPR.
  • Figure 4: A categorization of DRL approaches.
  • Figure 5: The general framework of variational auto-encoder (VAE).
  • ...and 7 more figures

Theorems & Definitions (2)

  • Definition 1
  • Definition 2