Wasserstein Loss for Semantic Editing in the Latent Space of GANs

• URL: http://arxiv.org/abs/2304.10508v1
• Date: Wed, 22 Mar 2023 08:15:27 GMT
• Title: Wasserstein Loss for Semantic Editing in the Latent Space of GANs
• Authors: Perla Doubinsky (CEDRIC - VERTIGO, CNAM), Nicolas Audebert (CEDRIC - VERTIGO, CNAM), Michel Crucianu (CEDRIC - VERTIGO), Herv\'e Le Borgne (CEA)
• Abstract summary: Different methods propose to learn edits in latent space corresponding to semantic attributes. Most supervised methods rely on the guidance of classifiers to produce such edits. We propose an alternative formulation based on the Wasserstein loss that avoids such problems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The latent space of GANs contains rich semantics reflecting the training data. Different methods propose to learn edits in latent space corresponding to semantic attributes, thus allowing to modify generated images. Most supervised methods rely on the guidance of classifiers to produce such edits. However, classifiers can lead to out-of-distribution regions and be fooled by adversarial samples. We propose an alternative formulation based on the Wasserstein loss that avoids such problems, while maintaining performance on-par with classifier-based approaches. We demonstrate the effectiveness of our method on two datasets (digits and faces) using StyleGAN2.

Related papers

• ContraFeat: Contrasting Deep Features for Semantic Discovery [102.4163768995288]
  StyleGAN has shown strong potential for disentangled semantic control. Existing semantic discovery methods on StyleGAN rely on manual selection of modified latent layers to obtain satisfactory manipulation results. We propose a model that automates this process and achieves state-of-the-art semantic discovery performance.
  arXiv  Detail & Related papers  (2022-12-14T15:22:13Z)
• On Robust Learning from Noisy Labels: A Permutation Layer Approach [53.798757734297986]
  This paper introduces a permutation layer learning approach termed PermLL to dynamically calibrate the training process of a deep neural network (DNN) We provide two variants of PermLL in this paper: one applies the permutation layer to the model's prediction, while the other applies it directly to the given noisy label. We validate PermLL experimentally and show that it achieves state-of-the-art performance on both real and synthetic datasets.
  arXiv  Detail & Related papers  (2022-11-29T03:01:48Z)
• 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)
• Unsupervised Contrastive Domain Adaptation for Semantic Segmentation [75.37470873764855]
  We introduce contrastive learning for feature alignment in cross-domain adaptation. The proposed approach consistently outperforms state-of-the-art methods for domain adaptation. It achieves 60.2% mIoU on the Cityscapes dataset.
  arXiv  Detail & Related papers  (2022-04-18T16:50:46Z)
• Learning Conditional Invariance through Cycle Consistency [60.85059977904014]
  We propose a novel approach to identify meaningful and independent factors of variation in a dataset. Our method involves two separate latent subspaces for the target property and the remaining input information. We demonstrate on synthetic and molecular data that our approach identifies more meaningful factors which lead to sparser and more interpretable models.
  arXiv  Detail & Related papers  (2021-11-25T17:33:12Z)
• Multi-Attribute Balanced Sampling for Disentangled GAN Controls [0.0]
  Various controls over the generated data can be extracted from the latent space of a pre-trained GAN. We show that this approach outperforms state-of-the-art classifier-based methods while avoiding the need for disentanglement-enforcing post-processing.
  arXiv  Detail & Related papers  (2021-10-28T08:44:13Z)
• Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation [12.191798515559622]
  Polarized-VAE is an approach that disentangles attributes in the latent space based on proximity measures. We apply our method to disentangle the semantics and syntax of sentences and carry out transfer experiments.
  arXiv  Detail & Related papers  (2020-04-22T19:26:09Z)
• Learning to Manipulate Individual Objects in an Image [71.55005356240761]
  We describe a method to train a generative model with latent factors that are independent and localized. This means that perturbing the latent variables affects only local regions of the synthesized image, corresponding to objects. Unlike other unsupervised generative models, ours enables object-centric manipulation, without requiring object-level annotations.
  arXiv  Detail & Related papers  (2020-04-11T21:50:20Z)
• Focus on Semantic Consistency for Cross-domain Crowd Understanding [34.560447389853614]
  Some domain adaptation algorithms try to liberate it by training models with synthetic data. We found that a mass of estimation errors in the background areas impede the performance of the existing methods. In this paper, we propose a domain adaptation method to eliminate it.
  arXiv  Detail & Related papers  (2020-02-20T08:51:05Z)

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.