Closed-Form Factorization of Latent Semantics in GANs

Abstract: A rich set of interpretable dimensions has been shown to emerge in the latent space of the Generative Adversarial Networks (GANs) trained for synthesizing images. In order to identify such latent dimensions for image editing, previous methods typically annotate a collection of synthesized samples and train linear classifiers in the latent space. However, they require a clear definition of the target attribute as well as the corresponding manual annotations, limiting their applications in practice. In this work, we examine the internal representation learned by GANs to reveal the underlying variation factors in an unsupervised manner. In particular, we take a closer look into the generation mechanism of GANs and further propose a closed-form factorization algorithm for latent semantic discovery by directly decomposing the pre-trained weights. With a lightning-fast implementation, our approach is capable of not only finding semantically meaningful dimensions comparably to the state-of-the-art supervised methods, but also resulting in far more versatile concepts across multiple GAN models trained on a wide range of datasets.

• The paper presents SeFa, a closed-form unsupervised method that factorizes the first layer weights in GANs to reveal latent semantic directions.
• It employs eigen-decomposition across various GAN architectures like PGGAN, StyleGAN, and BigGAN to uncover intuitive image attribute trends.
• The method achieves competitive results in manipulating features such as pose and gender without requiring retraining or auxiliary predictors.

Overview of "Closed-Form Factorization of Latent Semantics in GANs"

The paper "Closed-Form Factorization of Latent Semantics in GANs" by Yujun Shen and Bolei Zhou presents an unsupervised approach to discovering interpretable latent dimensions within Generative Adversarial Networks (GANs). While GANs have advanced image synthesis substantially, the understanding and manipulation of the latent spaces that drive these models remain complex. Traditional methods have relied on supervised techniques requiring labeled data and pre-defined classifiers to identify and manipulate these latent dimensions. This paper, however, introduces SeFa, a closed-form algorithm that obviates the need for data sampling or model retraining, thereby offering an efficient and unsupervised alternative.

Methodological Insights

The core of the proposed approach lies in the decomposition of pre-trained GAN model weights to unveil latent semantic directions. The authors propose that by factorizing the weight parameters of the first transformation layer in GANs, it's possible to identify semantically meaningful directions. This factorization is achieved by solving an optimization problem that maximizes the variation in the latent space, captured via the eigenvectors of the weight matrix.

The application of the method extends across various GAN architectures, including PGGAN, StyleGAN, and BigGAN. For each architecture, the paper demonstrates that SeFa can uncover diverse semantic directions that correspond to intuitive image attributes without any direct supervision.

Quantitative and Qualitative Analysis

The authors provide an extensive comparison between SeFa and existing methods, including both supervised (InterFaceGAN) and unsupervised (GANSpace, InfoGAN) approaches. SeFa exhibits competitive qualitative results in manipulating common attributes such as pose and gender. Notably, the method accomplishes this without auxiliary attribute predictors, showcasing its efficiency and broad applicability.

Through user studies and re-scoring analyses, SeFa's ability to find interpretable and variably impactful directions in the latent space is evaluated. Although the paper acknowledges some limitations in addressing fine-grained attributes (e.g., eyeglasses), the broader versatility and semantic diversity discovered by SeFa are underlined as significant advantages.

Implications and Speculations for Future Work

Practically, this research opens avenues for more interactive and generalizable image editing applications without the overhead of prior data annotation. Theoretically, it suggests that much of the semantic information in GANs is inherently available in pre-trained weights, prompting reconsideration of the necessity for heavy supervised approaches.

In the future, this work could influence advances in explainable AI by providing a framework for interpreting models' latent spaces more generally. Additionally, extending SeFa to dynamic or temporal data in generative models may lead to advancements in video synthesis and other complex domains.

Conclusion

The paper presents a robust, unsupervised approach to latent semantic factorization in GANs, demonstrating both efficiency and applicability across multiple generative models. As AI continues to evolve, techniques like SeFa that enable deeper insights into latent structures will be crucial for developing more transparent and versatile AI systems.