Diamond in the rough: Improving image realism by traversing the GAN latent space

• URL: http://arxiv.org/abs/2104.05518v1
• Date: Mon, 12 Apr 2021 14:45:29 GMT
• Title: Diamond in the rough: Improving image realism by traversing the GAN latent space
• Authors: Jeffrey Wen, Fabian Benitez-Quiroz, Qianli Feng, Aleix Martinez
• Abstract summary: We present an unsupervised method to find a direction in the latent space that aligns with improved photo-realism. Our approach leaves the network unchanged while enhancing the fidelity of the generated image. We use a simple generator inversion to find the direction in the latent space that results in the smallest change in the image space.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In just a few years, the photo-realism of images synthesized by Generative Adversarial Networks (GANs) has gone from somewhat reasonable to almost perfect largely by increasing the complexity of the networks, e.g., adding layers, intermediate latent spaces, style-transfer parameters, etc. This trajectory has led many of the state-of-the-art GANs to be inaccessibly large, disengaging many without large computational resources. Recognizing this, we explore a method for squeezing additional performance from existing, low-complexity GANs. Formally, we present an unsupervised method to find a direction in the latent space that aligns with improved photo-realism. Our approach leaves the network unchanged while enhancing the fidelity of the generated image. We use a simple generator inversion to find the direction in the latent space that results in the smallest change in the image space. Leveraging the learned structure of the latent space, we find moving in this direction corrects many image artifacts and brings the image into greater realism. We verify our findings qualitatively and quantitatively, showing an improvement in Frechet Inception Distance (FID) exists along our trajectory which surpasses the original GAN and other approaches including a supervised method. We expand further and provide an optimization method to automatically select latent vectors along the path that balance the variation and realism of samples. We apply our method to several diverse datasets and three architectures of varying complexity to illustrate the generalizability of our approach. By expanding the utility of low-complexity and existing networks, we hope to encourage the democratization of GANs.

Related papers

• Layered Rendering Diffusion Model for Zero-Shot Guided Image Synthesis [60.260724486834164]
  This paper introduces innovative solutions to enhance spatial controllability in diffusion models reliant on text queries. We present two key innovations: Vision Guidance and the Layered Rendering Diffusion framework. We apply our method to three practical applications: bounding box-to-image, semantic mask-to-image and image editing.
  arXiv  Detail & Related papers  (2023-11-30T10:36:19Z)
• PC-GANs: Progressive Compensation Generative Adversarial Networks for Pan-sharpening [50.943080184828524]
  We propose a novel two-step model for pan-sharpening that sharpens the MS image through the progressive compensation of the spatial and spectral information. The whole model is composed of triple GANs, and based on the specific architecture, a joint compensation loss function is designed to enable the triple GANs to be trained simultaneously.
  arXiv  Detail & Related papers  (2022-07-29T03:09:21Z)
• Deep Generalized Unfolding Networks for Image Restoration [16.943609020362395]
  We propose a Deep Generalized Unfolding Network (DGUNet) for image restoration. We integrate a gradient estimation strategy into the gradient descent step of the Proximal Gradient Descent (PGD) algorithm. Our method is superior in terms of state-of-the-art performance, interpretability, and generalizability.
  arXiv  Detail & Related papers  (2022-04-28T08:39:39Z)
• Enhancing Photorealism Enhancement [83.88433283714461]
  We present an approach to enhancing the realism of synthetic images using a convolutional network. We analyze scene layout distributions in commonly used datasets and find that they differ in important ways. We report substantial gains in stability and realism in comparison to recent image-to-image translation methods.
  arXiv  Detail & Related papers  (2021-05-10T19:00:49Z)
• The Geometry of Deep Generative Image Models and its Applications [0.0]
  Generative adversarial networks (GANs) have emerged as a powerful unsupervised method to model the statistical patterns of real-world data sets. These networks are trained to map random inputs in their latent space to new samples representative of the learned data. The structure of the latent space is hard to intuit due to its high dimensionality and the non-linearity of the generator.
  arXiv  Detail & Related papers  (2021-01-15T07:57:33Z)
• Unsupervised Discovery of Disentangled Manifolds in GANs [74.24771216154105]
  Interpretable generation process is beneficial to various image editing applications. We propose a framework to discover interpretable directions in the latent space given arbitrary pre-trained generative adversarial networks.
  arXiv  Detail & Related papers  (2020-11-24T02:18:08Z)
• Learning Enriched Features for Real Image Restoration and Enhancement [166.17296369600774]
  convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. We present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network. Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details.
  arXiv  Detail & Related papers  (2020-03-15T11:04:30Z)
• Image Fine-grained Inpainting [89.17316318927621]
  We present a one-stage model that utilizes dense combinations of dilated convolutions to obtain larger and more effective receptive fields. To better train this efficient generator, except for frequently-used VGG feature matching loss, we design a novel self-guided regression loss. We also employ a discriminator with local and global branches to ensure local-global contents consistency.
  arXiv  Detail & Related papers  (2020-02-07T03:45:25Z)

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.