3D GANs and Latent Space: A comprehensive survey
SP Tata, S Mishra - arXiv preprint arXiv:2304.03932, 2023 - arxiv.org
Generative Adversarial Networks (GANs) have emerged as a significant player in generative
modeling by mapping lower-dimensional random noise to higher-dimensional spaces …
modeling by mapping lower-dimensional random noise to higher-dimensional spaces …
Exploiting Topological Prior for Boosting Point Cloud Generation
B Chen - arXiv preprint arXiv:2403.10962, 2024 - arxiv.org
This paper presents an innovative enhancement to the Sphere as Prior Generative
Adversarial Network (SP-GAN) model, a state-of-the-art GAN designed for point cloud …
Adversarial Network (SP-GAN) model, a state-of-the-art GAN designed for point cloud …
[PDF][PDF] Equivariant Graph Neural Networks for amorphous materials
FP LANDES, C FURTLEHNER - 2021 - lri.fr
Abstract Graph Neural Networks (GNNs) are a relatively recent paradigm of Deep Learning,
allowing to enforce node-permutation equivariance (re-indexing), a symmetry present in all …
allowing to enforce node-permutation equivariance (re-indexing), a symmetry present in all …