WarpedGANSpace: Finding non-linear RBF paths in GAN latent space (2109.13357v1)

Abstract: This work addresses the problem of discovering, in an unsupervised manner, interpretable paths in the latent space of pretrained GANs, so as to provide an intuitive and easy way of controlling the underlying generative factors. In doing so, it addresses some of the limitations of the state-of-the-art works, namely, a) that they discover directions that are independent of the latent code, i.e., paths that are linear, and b) that their evaluation relies either on visual inspection or on laborious human labeling. More specifically, we propose to learn non-linear warpings on the latent space, each one parametrized by a set of RBF-based latent space warping functions, and where each warping gives rise to a family of non-linear paths via the gradient of the function. Building on the work of Voynov and Babenko, that discovers linear paths, we optimize the trainable parameters of the set of RBFs, so as that images that are generated by codes along different paths, are easily distinguishable by a discriminator network. This leads to easily distinguishable image transformations, such as pose and facial expressions in facial images. We show that linear paths can be derived as a special case of our method, and show experimentally that non-linear paths in the latent space lead to steeper, more disentangled and interpretable changes in the image space than in state-of-the art methods, both qualitatively and quantitatively. We make the code and the pretrained models publicly available at: https://github.com/chi0tzp/WarpedGANSpace.

Insights into "WarpedGANSpace: Finding non-linear RBF paths in GAN latent space"

The focus of this paper is on a novel method for discovering interpretable, non-linear paths within the latent space of pretrained Generative Adversarial Networks (GANs). This approach is rooted in the use of Radial Basis Functions (RBFs) to parameterize latent space warping functions, which overcome the limitations inherent in linear path discovery methods that rely heavily on visual inspections or manual labeling.

Methodological Overview

The method proposed in this paper introduces non-linear deformation of the latent space through RBF-based warpings. By considering the latent space as a vector field, each warping function determines a family of non-linear paths characterized by the gradients of the function. This contrasts starkly with previous methodologies which focus on deriving linear paths irrespective of the latent codes sampled — treating linearity as a simplifying assumption at the cost of potentially missing complex, non-linear transformations inherent to many generative processes encoded in GAN architectures.

Key here is an unsupervised learning framework that optimizes these warping functions to produce image transformations distinguishable by a discriminator network. The authors make a compelling case for using anisotropic warping functions, which they demonstrate unlocks pathways to more nuanced and interpretable generative factor control as opposed to isotropic, linear transformations.

Quantitative and Qualitative Outcomes

Quantitative benchmarks underline the efficacy of WarpedGANSpace paths, yielding steeper and more disentangled transformations in image space when compared to state-of-the-art, linear path discovery methods. For instance, classifiers trained to recognize various attributes (such as facial pose or expressions) have supported these claims, displaying significant improvement in attribute disentanglement over linear methods like GANSpace or previous unsupervised approaches.

Qualitatively, the paper presents transformations in image domains such as facial expressions and identity attributes demonstrating that WarpedGANSpace can navigate along complex transformations, like changing skin tone or facial expressions, without succumbing to entropic collapse or quality attrition typical in extensive latent space shifts.

Implications and Future Speculations

This research moves towards more nuanced, unsupervised exploration of GAN latent spaces, fostering avenues for applications in domains requiring fine-grained control over generated content. Potential implications include advancements in AI-driven media editing tools, customization of generative models in creative industries, and improvements in the explainability of GANs in contexts where interpretability is paramount.

Future research may build upon WarpedGANSpace by exploring alternative warping function families beyond RBFs, applying the approach to diversely trained GANs tackling non-visual domains, and integrating probabilistic models that would associate latent space trajectories with generative outcome distributions.

The presented WarpedGANSpace framework thus provides a substantial forward leap in autonomously navigating complex, non-linear dimensionality in GAN latent spaces, enhancing both the interpretability and controllability of high-dimensional generative models.