Eigenvector Fields for Latent Space Discovery
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| Natura: | Recurso digital |
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Zenodo
2025
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| _version_ | 1866901476365828096 |
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| author | SÉRGIO DE ANDRADE, PAULO |
| author_facet | SÉRGIO DE ANDRADE, PAULO |
| contents | Deep generative models have demonstrated remarkable success in synthesizing high-fidelity data, yet understanding and controlling their internal latent spaces remains a significant challenge. This paper introduces a novel, unsupervised method for discovering meaningful and disentangled directions of variation within the latent space of pre-trained generative models. Our approach constructs a local affinity graph based on the semantic similarity of generated outputs for a neighborhood of latent space samples. By computing the eigenvectors of the graph Laplacian, we derive a set of orthogonal direction vectors that constitute a local vector field. These eigenvector fields correspond to the principal axes of semantic variation in the output space, providing an interpretable coordinate system for navigating the latent manifold. We demonstrate empirically, using a pre-trained StyleGAN model on the FFHQ dataset, that traversing the latent space along these eigenvector directions leads to coherent and disentangled semantic changes in the generated images, such as modifications to age, expression, and pose. Qualitative and quantitative results show that our method outperforms baseline approaches like Principal Component Analysis in identifying semantically rich and disentangled latent directions without requiring any external labels or classifiers. This work provides a principled geometric framework for enhancing the interpretability and controllability of deep generative models. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17682214 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Eigenvector Fields for Latent Space Discovery SÉRGIO DE ANDRADE, PAULO Deep generative models have demonstrated remarkable success in synthesizing high-fidelity data, yet understanding and controlling their internal latent spaces remains a significant challenge. This paper introduces a novel, unsupervised method for discovering meaningful and disentangled directions of variation within the latent space of pre-trained generative models. Our approach constructs a local affinity graph based on the semantic similarity of generated outputs for a neighborhood of latent space samples. By computing the eigenvectors of the graph Laplacian, we derive a set of orthogonal direction vectors that constitute a local vector field. These eigenvector fields correspond to the principal axes of semantic variation in the output space, providing an interpretable coordinate system for navigating the latent manifold. We demonstrate empirically, using a pre-trained StyleGAN model on the FFHQ dataset, that traversing the latent space along these eigenvector directions leads to coherent and disentangled semantic changes in the generated images, such as modifications to age, expression, and pose. Qualitative and quantitative results show that our method outperforms baseline approaches like Principal Component Analysis in identifying semantically rich and disentangled latent directions without requiring any external labels or classifiers. This work provides a principled geometric framework for enhancing the interpretability and controllability of deep generative models. |
| title | Eigenvector Fields for Latent Space Discovery |
| url | https://doi.org/10.5281/zenodo.17682214 |