You Only Need Adversarial Supervision for Semantic Image Synthesis

• URL: http://arxiv.org/abs/2012.04781v3
• Date: Fri, 19 Mar 2021 23:35:42 GMT
• Title: You Only Need Adversarial Supervision for Semantic Image Synthesis
• Authors: Vadim Sushko, Edgar Sch\"onfeld, Dan Zhang, Juergen Gall, Bernt Schiele, Anna Khoreva
• Abstract summary: We propose a novel, simplified GAN model, which needs only adversarial supervision to achieve high quality results. We show that images synthesized by our model are more diverse and follow the color and texture of real images more closely.
• Score: 84.83711654797342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite their recent successes, GAN models for semantic image synthesis still suffer from poor image quality when trained with only adversarial supervision. Historically, additionally employing the VGG-based perceptual loss has helped to overcome this issue, significantly improving the synthesis quality, but at the same time limiting the progress of GAN models for semantic image synthesis. In this work, we propose a novel, simplified GAN model, which needs only adversarial supervision to achieve high quality results. We re-design the discriminator as a semantic segmentation network, directly using the given semantic label maps as the ground truth for training. By providing stronger supervision to the discriminator as well as to the generator through spatially- and semantically-aware discriminator feedback, we are able to synthesize images of higher fidelity with better alignment to their input label maps, making the use of the perceptual loss superfluous. Moreover, we enable high-quality multi-modal image synthesis through global and local sampling of a 3D noise tensor injected into the generator, which allows complete or partial image change. We show that images synthesized by our model are more diverse and follow the color and texture distributions of real images more closely. We achieve an average improvement of $6$ FID and $5$ mIoU points over the state of the art across different datasets using only adversarial supervision.

Related papers

• Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization [62.157627519792946]
  We introduce a novel framework called bridged transfer, which initially employs synthetic images for fine-tuning a pre-trained model to improve its transferability. We propose dataset style inversion strategy to improve the stylistic alignment between synthetic and real images. Our proposed methods are evaluated across 10 different datasets and 5 distinct models, demonstrating consistent improvements.
  arXiv  Detail & Related papers  (2024-03-28T22:25:05Z)
• Unlocking Pre-trained Image Backbones for Semantic Image Synthesis [29.688029979801577]
  We propose a new class of GAN discriminators for semantic image synthesis that generates highly realistic images. Our model, which we dub DP-SIMS, achieves state-of-the-art results in terms of image quality and consistency with the input label maps on ADE-20K, COCO-Stuff, and Cityscapes.
  arXiv  Detail & Related papers  (2023-12-20T09:39:19Z)
• Wavelet-based Unsupervised Label-to-Image Translation [9.339522647331334]
  We propose a new Unsupervised paradigm for SIS (USIS) that makes use of a self-supervised segmentation loss and whole image wavelet based discrimination. We test our methodology on 3 challenging datasets and demonstrate its ability to bridge the performance gap between paired and unpaired models.
  arXiv  Detail & Related papers  (2023-05-16T17:48:44Z)
• Semantic Image Synthesis via Diffusion Models [159.4285444680301]
  Denoising Diffusion Probabilistic Models (DDPMs) have achieved remarkable success in various image generation tasks. Recent work on semantic image synthesis mainly follows the emphde facto Generative Adversarial Nets (GANs)
  arXiv  Detail & Related papers  (2022-06-30T18:31:51Z)
• High-Quality Pluralistic Image Completion via Code Shared VQGAN [51.7805154545948]
  We present a novel framework for pluralistic image completion that can achieve both high quality and diversity at much faster inference speed. Our framework is able to learn semantically-rich discrete codes efficiently and robustly, resulting in much better image reconstruction quality.
  arXiv  Detail & Related papers  (2022-04-05T01:47:35Z)
• A Shared Representation for Photorealistic Driving Simulators [83.5985178314263]
  We propose to improve the quality of generated images by rethinking the discriminator architecture. The focus is on the class of problems where images are generated given semantic inputs, such as scene segmentation maps or human body poses. We aim to learn a shared latent representation that encodes enough information to jointly do semantic segmentation, content reconstruction, along with a coarse-to-fine grained adversarial reasoning.
  arXiv  Detail & Related papers  (2021-12-09T18:59:21Z)
• USIS: Unsupervised Semantic Image Synthesis [9.613134538472801]
  We propose a new Unsupervised paradigm for Semantic Image Synthesis (USIS) USIS learns to output images with visually separable semantic classes using a self-supervised segmentation loss. In order to match the color and texture distribution of real images without losing high-frequency information, we propose to use whole image wavelet-based discrimination.
  arXiv  Detail & Related papers  (2021-09-29T20:48:41Z)
• Improving Augmentation and Evaluation Schemes for Semantic Image Synthesis [16.097324852253912]
  We introduce a novel augmentation scheme designed specifically for generative adversarial networks (GANs) We propose to randomly warp object shapes in the semantic label maps used as an input to the generator. The local shape discrepancies between the warped and non-warped label maps and images enable the GAN to learn better the structural and geometric details of the scene.
  arXiv  Detail & Related papers  (2020-11-25T10:55:26Z)
• 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.