GAN Inversion for Image Editing via Unsupervised Domain Adaptation

• URL: http://arxiv.org/abs/2211.12123v3
• Date: Thu, 30 May 2024 00:47:54 GMT
• Title: GAN Inversion for Image Editing via Unsupervised Domain Adaptation
• Authors: Siyu Xing, Chen Gong, Hewei Guo, Xiao-Yu Zhang, Xinwen Hou, Yu Liu,
• Abstract summary: We propose Unsupervised Domain Adaptation (UDA) in the inversion process, namely UDA-inversion, for effective inversion and editing of both HQ and LQ images. UDA-Inversion achieves a better PSNR of 22.14 on FFHQ dataset and performs comparably to supervised methods.
• Score: 18.328386420520978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing GAN inversion methods work brilliantly in reconstructing high-quality (HQ) images while struggling with more common low-quality (LQ) inputs in practical application. To address this issue, we propose Unsupervised Domain Adaptation (UDA) in the inversion process, namely UDA-inversion, for effective inversion and editing of both HQ and LQ images. Regarding unpaired HQ images as the source domain and LQ images as the unlabeled target domain, we introduce a theoretical guarantee: loss value in the target domain is upper-bounded by loss in the source domain and a novel discrepancy function measuring the difference between two domains. Following that, we can only minimize this upper bound to obtain accurate latent codes for HQ and LQ images. Thus, constructive representations of HQ images can be spontaneously learned and transformed into LQ images without supervision. UDA-Inversion achieves a better PSNR of 22.14 on FFHQ dataset and performs comparably to supervised methods.

Related papers

• In-Domain GAN Inversion for Faithful Reconstruction and Editability [132.68255553099834]
  We propose in-domain GAN inversion, which consists of a domain-guided domain-regularized and a encoder to regularize the inverted code in the native latent space of the pre-trained GAN model. We make comprehensive analyses on the effects of the encoder structure, the starting inversion point, as well as the inversion parameter space, and observe the trade-off between the reconstruction quality and the editing property.
  arXiv  Detail & Related papers  (2023-09-25T08:42:06Z)
• Dual Associated Encoder for Face Restoration [68.49568459672076]
  We propose a novel dual-branch framework named DAEFR to restore facial details from low-quality (LQ) images. Our method introduces an auxiliary LQ branch that extracts crucial information from the LQ inputs. We evaluate the effectiveness of DAEFR on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-08-14T17:58:33Z)
• HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidance [45.84780456554191]
  Unpaired Medical Image Enhancement (UMIE) aims to transform a low-quality (LQ) medical image into a high-quality (HQ) one without relying on paired images for training. We propose a novel UMIE approach that avoids the above limitation of existing methods by directly encoding HQ cues into the LQ enhancement process. We train the enhancement network adversarially with a discriminator to ensure the generated HQ image falls into the HQ domain.
  arXiv  Detail & Related papers  (2023-07-15T15:26:25Z)
• I2F: A Unified Image-to-Feature Approach for Domain Adaptive Semantic Segmentation [55.633859439375044]
  Unsupervised domain adaptation (UDA) for semantic segmentation is a promising task freeing people from heavy annotation work. Key idea to tackle this problem is to perform both image-level and feature-level adaptation jointly. This paper proposes a novel UDA pipeline for semantic segmentation that unifies image-level and feature-level adaptation.
  arXiv  Detail & Related papers  (2023-01-03T15:19:48Z)
• High-Fidelity GAN Inversion for Image Attribute Editing [61.966946442222735]
  We present a novel high-fidelity generative adversarial network (GAN) inversion framework that enables attribute editing with image-specific details well-preserved. With a low bit-rate latent code, previous works have difficulties in preserving high-fidelity details in reconstructed and edited images. We propose a distortion consultation approach that employs a distortion map as a reference for high-fidelity reconstruction.
  arXiv  Detail & Related papers  (2021-09-14T11:23:48Z)
• DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation [97.74059510314554]
  Unsupervised domain adaptation (UDA) for semantic segmentation aims to adapt a segmentation model trained on the labeled source domain to the unlabeled target domain. Existing methods try to learn domain invariant features while suffering from large domain gaps. We propose a novel Dual Soft-Paste (DSP) method in this paper.
  arXiv  Detail & Related papers  (2021-07-20T16:22:40Z)
• Super-Resolving Cross-Domain Face Miniatures by Peeking at One-Shot Exemplar [42.78574493628936]
  We develop a Domain-Aware Pyramid-based Face Super-Resolution network, named DAP-FSR network. Our DAP-FSR is the first attempt to super-resolve LR faces from a target domain by exploiting only a pair of high-resolution (HR) and LR exemplars in the target domain. By iteratively updating the latent representations and our decoder, our DAP-FSR will be adapted to the target domain.
  arXiv  Detail & Related papers  (2021-03-16T05:47:26Z)
• Image Inpainting with Learnable Feature Imputation [8.293345261434943]
  A regular convolution layer applying a filter in the same way over known and unknown areas causes visual artifacts in the inpainted image. We propose (layer-wise) feature imputation of the missing input values to a convolution. We present comparisons on CelebA-HQ and Places2 to current state-of-the-art to validate our model.
  arXiv  Detail & Related papers  (2020-11-02T16:05:32Z)
• In-Domain GAN Inversion for Real Image Editing [56.924323432048304]
  A common practice of feeding a real image to a trained GAN generator is to invert it back to a latent code. Existing inversion methods typically focus on reconstructing the target image by pixel values yet fail to land the inverted code in the semantic domain of the original latent space. We propose an in-domain GAN inversion approach, which faithfully reconstructs the input image and ensures the inverted code to be semantically meaningful for editing.
  arXiv  Detail & Related papers  (2020-03-31T18:20:18Z)

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.