Semantic Hierarchy Emerges in Deep Generative Representations for Scene Synthesis (1911.09267v3)

Abstract: Despite the success of Generative Adversarial Networks (GANs) in image synthesis, there lacks enough understanding on what generative models have learned inside the deep generative representations and how photo-realistic images are able to be composed of the layer-wise stochasticity introduced in recent GANs. In this work, we show that highly-structured semantic hierarchy emerges as variation factors from synthesizing scenes from the generative representations in state-of-the-art GAN models, like StyleGAN and BigGAN. By probing the layer-wise representations with a broad set of semantics at different abstraction levels, we are able to quantify the causality between the activations and semantics occurring in the output image. Such a quantification identifies the human-understandable variation factors learned by GANs to compose scenes. The qualitative and quantitative results further suggest that the generative representations learned by the GANs with layer-wise latent codes are specialized to synthesize different hierarchical semantics: the early layers tend to determine the spatial layout and configuration, the middle layers control the categorical objects, and the later layers finally render the scene attributes as well as color scheme. Identifying such a set of manipulatable latent variation factors facilitates semantic scene manipulation.

• The paper demonstrates that GANs inherently develop layered semantic hierarchies, allowing detailed control over scene layout, object classes, and attribute nuances.
• It introduces a probing framework using trained classifiers to map activations to linear decision boundaries, quantifying how semantics emerge in deep generative models.
• These insights pave the way for enhanced image synthesis and editing techniques, fostering advancements in computer vision and graphics.

Semantic Hierarchy in Deep Generative Models for Scene Synthesis

The paper "Semantic Hierarchy Emerges in Deep Generative Representations for Scene Synthesis" explores the intricacies of how generative models, particularly Generative Adversarial Networks (GANs), encode and synthesize detailed semantics across various abstraction levels. This research stands out for its methodical approach to identifying emergent semantic hierarchies within deep generative models and provides a pathway for manipulating synthesized scenes through these latent representations.

Overview and Objectives

The primary aim of this paper is to explore and quantify the semantic structures emerging within state-of-the-art GANs like StyleGAN and BigGAN. These hierarchies are dissected across multiple layers of abstraction within the GAN's generative process—spanning the spatial layout, categorical objects, scene attributes, and color schemes. The authors propose a probing and verification framework to assess causality between layer activations and semantic occurrences, laying the foundation for understanding and manipulating photo-realistic outputs.

Framework and Methodology

The proposed framework operates through a two-fold process: probing and verifying. Initially, the latent space of the GAN is probed with trained classifiers at different abstraction levels, mapping them onto a comprehensive semantic space. Following this, a linear decision boundary is identified for each semantic concept, allowing the researchers to quantify these emergent semantics in the context of generative models. This technique allows precise and semantic-directed scene manipulation by aligning latent space variations to human-recognizable concepts.

Key Findings

Findings indicate a distinct generative hierarchy within GANs, where different layers specialize in various semantic tasks. Early layers govern the scene's spatial layout, middle layers manage category-specific objects, and upper layers are tasked with details such as scene attributes and color palettes. Notably, the comprehensive assessment through human perception parallels these machine operations, giving credence to the quantification approach applied in this research.

Moreover, the paper illustrates the proficiency of GANs in generating shared and distinctive objects between various scene categories, enabling transformative yet consistent manipulations. This capability lays groundwork for category-overwriting, showcasing the potential for extensive applications in realistic scene synthesis.

Implications and Future Directions

This research has significant implications for the generation and editing of high-quality images. By elucidating the nature of semantic encoding in GANs, the paper provides essential insights that can enhance image synthesis's controllability and reliability. The emergence of these layer-wise semantic hierarchies hints at a robust mechanism underpinning the generation process in GANs, promoting the development of more refined models.

Future work could probe into enhancing the precision of off-the-shelf classifiers for better semantic boundary formation or diversifying architectures to optimize semantic discrimination in generative workflows. Furthermore, integrating computational photography could expand applications into real-world image rendering and editing, enhancing the flexibility and applicability of generative models.

Conclusion

In conclusion, the paper successfully bridges the gap between deep representation learning and semantic interpretation in scene synthesis. By analyzing and manipulating the emergent semantic hierarchies within GANs, the paper not only underscores the composed complexity inherent in advanced generative models but also illuminates pathways for refined editing and transformative applications in the domains of computer vision and graphics.