Multi-level Latent Space Structuring for Generative Control

• URL: http://arxiv.org/abs/2202.05910v1
• Date: Fri, 11 Feb 2022 21:26:17 GMT
• Title: Multi-level Latent Space Structuring for Generative Control
• Authors: Oren Katzir, Vicky Perepelook, Dani Lischinski and Daniel Cohen-Or
• Abstract summary: We propose to leverage the StyleGAN generative architecture to devise a new truncation technique. We do so by learning to re-generate W-space, the extended intermediate latent space of StyleGAN, using a learnable mixture of Gaussians. The resulting truncation scheme is more faithful to the original untruncated samples and allows a better trade-off between quality and diversity.
• Score: 53.240701050423155
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Truncation is widely used in generative models for improving the quality of the generated samples, at the expense of reducing their diversity. We propose to leverage the StyleGAN generative architecture to devise a new truncation technique, based on a decomposition of the latent space into clusters, enabling customized truncation to be performed at multiple semantic levels. We do so by learning to re-generate W-space, the extended intermediate latent space of StyleGAN, using a learnable mixture of Gaussians, while simultaneously training a classifier to identify, for each latent vector, the cluster that it belongs to. The resulting truncation scheme is more faithful to the original untruncated samples and allows a better trade-off between quality and diversity. We compare our method to other truncation approaches for StyleGAN, both qualitatively and quantitatively.

Related papers

• GCC: Generative Calibration Clustering [55.44944397168619]
  We propose a novel Generative Clustering (GCC) method to incorporate feature learning and augmentation into clustering procedure. First, we develop a discrimirative feature alignment mechanism to discover intrinsic relationship across real and generated samples. Second, we design a self-supervised metric learning to generate more reliable cluster assignment.
  arXiv  Detail & Related papers  (2024-04-14T01:51:11Z)
• StyleGenes: Discrete and Efficient Latent Distributions for GANs [149.0290830305808]
  We propose a discrete latent distribution for Generative Adversarial Networks (GANs) Instead of drawing latent vectors from a continuous prior, we sample from a finite set of learnable latents. We take inspiration from the encoding of information in biological organisms.
  arXiv  Detail & Related papers  (2023-04-30T23:28:46Z)
• A Geometric Perspective on Variational Autoencoders [0.0]
  This paper introduces a new interpretation of the Variational Autoencoder framework by taking a fully geometric point of view. We show that using this scheme can make a vanilla VAE competitive and even better than more advanced versions on several benchmark datasets.
  arXiv  Detail & Related papers  (2022-09-15T15:32:43Z)
• GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learning [55.79997930181418]
  Generalized Zero-Shot Learning aims to recognize images from both the seen and unseen classes by transferring semantic knowledge from seen to unseen classes. It is a promising solution to take the advantage of generative models to hallucinate realistic unseen samples based on the knowledge learned from the seen classes. We propose a novel flow-based generative framework that consists of multiple conditional affine coupling layers for learning unseen data generation.
  arXiv  Detail & Related papers  (2022-07-05T04:04:37Z)
• One-Shot Adaptation of GAN in Just One CLIP [51.188396199083336]
  We present a novel single-shot GAN adaptation method through unified CLIP space manipulations. Specifically, our model employs a two-step training strategy: reference image search in the source generator using a CLIP-guided latent optimization. We show that our model generates diverse outputs with the target texture and outperforms the baseline models both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2022-03-17T13:03:06Z)
• Consistency and Diversity induced Human Motion Segmentation [231.36289425663702]
  We propose a novel Consistency and Diversity induced human Motion (CDMS) algorithm. Our model factorizes the source and target data into distinct multi-layer feature spaces. A multi-mutual learning strategy is carried out to reduce the domain gap between the source and target data.
  arXiv  Detail & Related papers  (2022-02-10T06:23:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.