Orthogonal Subspace Representation for Generative Adversarial Networks.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Computer Vision

Background:

• Disentanglement learning separates data variations for better inference.
• Generative adversarial networks (GANs) are used for learning interpretable representations.
• Existing GAN-based methods often neglect latent subspace properties, limiting factor independence.

Purpose of the Study:

• To propose a unified framework for disentanglement learning by investigating latent subspace learning (SL) in GANs.
• To introduce a novel GAN-based architecture, OSRGAN, for orthogonal subspace representation (OSR).
• To enhance the independence and interpretability of explanatory factors in data representations.

Main Methods:

• Developed OSRGAN, a GAN architecture incorporating orthogonal subspace representation (OSR).
• Implemented a three-stage OSR process: self-latent-aware, orthogonal subspace-aware, and structure representation-aware.
• Utilized alternating optimization for balanced training of correlation and orthogonality constraints.

Main Results:

• OSRGAN demonstrated improved disentanglement scores compared to state-of-the-art methods on various datasets and metrics (FactorVAE, SAP, MIG, VP).
• Theoretical analysis confirmed OSR enhances factor independence and interpretability.
• Convergence analysis showed enhanced stability and higher disentanglement performance.

Conclusions:

• The proposed OSRGAN framework offers a significant advancement in disentanglement learning by effectively utilizing latent subspace properties.
• OSRGAN achieves superior performance in separating and representing independent factors of variation.
• The method provides a robust and efficient approach for learning disentangled representations in GANs.