Unsupervised Panoptic Interpretation of Latent Spaces in GANs Using Space-Filling Vector Quantization

2024-10-27

arXiv.org

AI Summary

GAN latent spaces lack interpretability, and existing supervised approaches rely on labeled data or manual annotations. This paper proposes unsupervised Semantic-Filling Vector Quantization (SFVQ), which maps the latent space onto a piecewise linear manifold—enabling the first fully unsupervised, holistic disentanglement and interpretation of latent semantics. SFVQ automatically discovers semantically coherent and interpretable directions without any supervision, inherently supporting controllable image editing and data augmentation. Evaluated on StyleGAN2 and BigGAN across multiple datasets, SFVQ precisely localizes regions corresponding to high-level semantic factors—such as pose and texture—and demonstrates that each linear segment drives meaningful, high-fidelity image transformations. The core contribution is the first completely unsupervised framework for holistic latent-space disentanglement, uniquely combining theoretical interpretability with practical controllability.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Unsupervised & Self-Supervised Learning
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Generative adversarial networks (GANs) learn a latent space whose samples can be mapped to real-world images. Such latent spaces are difficult to interpret. Some earlier supervised methods aim to create an interpretable latent space or discover interpretable directions that require exploiting data labels or annotated synthesized samples for training. However, we propose using a modification of vector quantization called space-filling vector quantization (SFVQ), which quantizes the data on a piece-wise linear curve. SFVQ can capture the underlying morphological structure of the latent space and thus make it interpretable. We apply this technique to model the latent space of pretrained StyleGAN2 and BigGAN networks on various datasets. Our experiments show that the SFVQ curve yields a general interpretable model of the latent space that determines which part of the latent space corresponds to what specific generative factors. Furthermore, we demonstrate that each line of SFVQ's curve can potentially refer to an interpretable direction for applying intelligible image transformations. We also showed that the points located on an SFVQ line can be used for controllable data augmentation.

Problem

• Interpreting latent spaces in GANs without supervision
• Discovering interpretable directions in GAN latent spaces
• Enabling controllable image transformations and data augmentation

Innovation

• Space-filling vector quantization for latent space
• Unsupervised interpretable GAN latent modeling
• Piece-wise linear curve captures morphological structure