Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection

High-fidelity 3D GAN Inversion by Pseudo-multi-view Optimization

We present a high-fidelity 3D generative adversarial network (GAN) inversion framework that can synthesize photo-realistic novel views while preserving specific details of the input image. High-fidelity 3D GAN inversion is inherently challenging due to the geometry-texture trade-off in 3D inversion, where overfitting to a single view input image often damages the estimated geometry during the latent optimization. To solve this challenge, we propose a novel pipeline that builds on the pseudo-multi-view estimation with visibility analysis. We keep the original textures for the visible parts and …

High-Fidelity Image Inpainting with GAN Inversion

Image inpainting seeks a semantically consistent way to recover the corrupted image in the light of its unmasked content. Previous approaches usually reuse the well-trained GAN as effective prior to generate realistic patches for missing holes with GAN inversion. Nevertheless, the ignorance of a hard constraint in these algorithms may yield the gap between GAN inversion and image inpainting. Addressing this problem, in this paper, we devise a novel GAN inversion model for image inpainting, dubbed InvertFill, mainly consisting of an encoder with a pre-modulation module and a GAN generator with …

用于高保真图像合成的分层组合GAN

Hierarchy Composition GAN for High-fidelity Image Synthesis

针对现有GAN仅在几何或外观单一域工作导致合成伪影的问题，提出端到端HIC-GAN，通过分层组合与注意掩码机制统一两域建模，在多类图像任务上实现更优的高保真合成效果。

High-fidelity GAN Inversion with Padding Space

Inverting a Generative Adversarial Network (GAN) facilitates a wide range of image editing tasks using pre-trained generators. Existing methods typically employ the latent space of GANs as the inversion space yet observe the insufficient recovery of spatial details. In this work, we propose to involve the padding space of the generator to complement the latent space with spatial information. Concretely, we replace the constant padding (e.g., usually zeros) used in convolution layers with some instance-aware coefficients. In this way, the inductive bias assumed in the pre-trained model can be a…

基于多模态引导GAN逆变换的高保真图像修复

High-Fidelity Image Inpainting with Multimodal Guided GAN Inversion

针对现有GAN逆变换图像修复方法忽略未掩码区域一致性约束及单模态限制的问题，提出MMInvertFill，通过多模态引导编码器、F&W+潜在空间及软更新均值潜在模块，在六个数据集上超越现有方法，有效支持域外图像补全。

基于ProGAN的金属零件工业视觉检测合成图像生成

ProGAN-Based Synthetic Image Generation for Industrial Visual Inspection of Metallic Parts

该研究针对金属部件视觉检测的训练数据稀缺问题，采用带Wasserstein损失与梯度惩罚的ProGAN生成合成图像，经MVTec AD、MPDD数据集验证，生成图像可扩充训练数据、提升鲁棒性并降低成本。

One-class Steel Detector Using Patch GAN Discriminator for Visualising Anomalous Feature Map

For steel product manufacturing in indoor factories, steel defect detection is important for quality control. For example, a steel sheet is extremely delicate, and must be accurately inspected. However, to maintain the painted steel parts of the infrastructure around a severe outdoor environment, corrosion detection is critical for predictive maintenance. In this paper, we propose a general-purpose application for steel anomaly detection that consists of the following four components. The first, a learner, is a unit image classification network to determine whether the region of interest or ba…

Discovering Class-Specific GAN Controls for Semantic Image Synthesis

Prior work has extensively studied the latent space structure of GANs for unconditional image synthesis, enabling global editing of generated images by the unsupervised discovery of interpretable latent directions. However, the discovery of latent directions for conditional GANs for semantic image synthesis (SIS) has remained unexplored. In this work, we specifically focus on addressing this gap. We propose a novel optimization method for finding spatially disentangled class-specific directions in the latent space of pretrained SIS models. We show that the latent directions found by our method…

Learning a Deep Reinforcement Learning Policy Over the Latent Space of a Pre-trained GAN for Semantic Age Manipulation

Learning a disentangled representation of the latent space has become one of the most fundamental problems studied in computer vision. Recently, many Generative Adversarial Networks (GANs) have shown promising results in generating high fidelity images. However, studies to understand the semantic layout of the latent space of pre-trained models are still limited. Several works train conditional GANs to generate faces with required semantic attributes. Unfortunately, in these attempts, the generated output is often not as photo-realistic as the unconditional state-of-the-art models. Besides, th…

Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis

Training Generative Adversarial Networks (GAN) on high-fidelity images usually requires large-scale GPU-clusters and a vast number of training images. In this paper, we study the few-shot image synthesis task for GAN with minimum computing cost. We propose a light-weight GAN structure that gains superior quality on 1024*1024 resolution. Notably, the model converges from scratch with just a few hours of training on a single RTX-2080 GPU, and has a consistent performance, even with less than 100 training samples. Two technique designs constitute our work, a skip-layer channel-wise excitation mod…

Fast-GANFIT: Generative Adversarial Network for High Fidelity 3D Face Reconstruction

A lot of work has been done towards reconstructing the 3D facial structure from single images by capitalizing on the power of Deep Convolutional Neural Networks (DCNNs). In the recent works, the texture features either correspond to components of a linear texture space or are learned by auto-encoders directly from in-the-wild images. In all cases, the quality of the facial texture reconstruction is still not capable of modeling facial texture with high-frequency details. In this paper, we take a radically different approach and harness the power of Generative Adversarial Networks (GANs) and DC…

GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction

In the past few years, a lot of work has been done towards reconstructing the 3D facial structure from single images by capitalizing on the power of Deep Convolutional Neural Networks (DCNNs). In the most recent works, differentiable renderers were employed in order to learn the relationship between the facial identity features and the parameters of a 3D morphable model for shape and texture. The texture features either correspond to components of a linear texture space or are learned by auto-encoders directly from in-the-wild images. In all cases, the quality of the facial texture reconstruct…

Noise Homogenization via Multi-Channel Wavelet Filtering for High-Fidelity Sample Generation in GANs

In the generator of typical Generative Adversarial Networks (GANs), a noise is inputted to generate fake samples via a series of convolutional operations. However, current noise generation models merely relies on the information from the pixel space, which increases the difficulty to approach the target distribution. Fortunately, the long proven wavelet transformation is able to decompose multiple spectral information from the images. In this work, we propose a novel multi-channel wavelet-based filtering method for GANs, to cope with this problem. When embedding a wavelet deconvolution layer i…

基于可逆性分解的域外 GAN 反演用于照片级真实感人脸操控

Out-of-domain GAN inversion via Invertibility Decomposition for Photo-Realistic Human Face Manipulation

针对 GAN 人脸反演中 OOD 区域导致掩码不准的问题，提出联合学习可逆性检测与空间对齐的分解框架，提升反演和属性操控保真度。

Online Multi-Granularity Distillation for GAN Compression

Generative Adversarial Networks (GANs) have witnessed prevailing success in yielding outstanding images, however, they are burdensome to deploy on resource-constrained devices due to ponderous computational costs and hulking memory usage. Although recent efforts on compressing GANs have acquired remarkable results, they still exist potential model redundancies and can be further compressed. To solve this issue, we propose a novel online multi-granularity distillation (OMGD) scheme to obtain lightweight GANs, which contributes to generating high-fidelity images with low computational demands. W…

WINE: 用于高保真度优化的小波引导GAN反演与编辑

WINE: Wavelet-Guided GAN Inversion and Editing for High-Fidelity Refinement

针对现有GAN反演模型偏向低频信息、难以重建图像细节的问题，提出首个频域解读的WINE模型，通过小波损失与融合方案传递高频信息，在细节保留、图像质量及编辑场景的性能平衡上优于现有SOTA模型。

From Continuity to Editability: Inverting GANs with Consecutive Images

Existing GAN inversion methods are stuck in a paradox that the inverted codes can either achieve high-fidelity reconstruction, or retain the editing capability. Having only one of them clearly cannot realize real image editing. In this paper, we resolve this paradox by introducing consecutive images (\eg, video frames or the same person with different poses) into the inversion process. The rationale behind our solution is that the continuity of consecutive images leads to inherent editable directions. This inborn property is used for two unique purposes: 1) regularizing the joint inversion pro…

AvatarTex：从单张图像风格化头像进行高保真面部纹理重建

AvatarTex: High-Fidelity Facial Texture Reconstruction from Single-Image Stylized Avatars

AvatarTex提出三阶段扩散到GAN流水线，结合扩散模型的纹理多样性与GAN的UV拓扑一致性，并引入含2万张多风格UV纹理的TexHub数据集，实现从单张图像进行高保真风格化面部纹理重建。

How good is my GAN?

Generative adversarial networks (GANs) are one of the most popular methods for generating images today. While impressive results have been validated by visual inspection, a number of quantitative criteria have emerged only recently. We argue here that the existing ones are insufficient and need to be in adequation with the task at hand. In this paper we introduce two measures based on image classification---GAN-train and GAN-test, which approximate the recall (diversity) and precision (quality of the image) of GANs respectively. We evaluate a number of recent GAN approaches based on these two …

GAN-Based Facial Attractiveness Enhancement

We propose a generative framework based on generative adversarial network (GAN) to enhance facial attractiveness while preserving facial identity and high-fidelity. Given a portrait image as input, having applied gradient descent to recover a latent vector that this generative framework can use to synthesize an image resemble to the input image, beauty semantic editing manipulation on the corresponding recovered latent vector based on InterFaceGAN enables this framework to achieve facial image beautification. This paper compared our system with Beholder-GAN and our proposed result-enhanced ver…

STS-GAN：我们能否从任意2D样本合成高保真实体纹理？

STS-GAN: Can We Synthesize Solid Texture with High Fidelity from Arbitrary 2D Exemplar?

针对现有方法难以从任意2D样本生成高保真3D实体纹理的问题，提出基于GAN的STS-GAN框架，通过多尺度2D判别器约束3D生成器，实验证明可合成视觉特征一致的高保真实体纹理。

Synthetic Image Augmentation for Damage Region Segmentation using Conditional GAN with Structure Edge

Recently, social infrastructure is aging, and its predictive maintenance has become important issue. To monitor the state of infrastructures, bridge inspection is performed by human eye or bay drone. For diagnosis, primary damage region are recognized for repair targets. But, the degradation at worse level has rarely occurred, and the damage regions of interest are often narrow, so their ratio per image is extremely small pixel count, as experienced 0.6 to 1.5 percent. The both scarcity and imbalance property on the damage region of interest influences limited performance to detect damage. If …

A two-step machine learning approach for crop disease detection: an application of GAN and UAV technology

Automated plant diagnosis is a technology that promises large increases in cost-efficiency for agriculture. However, multiple problems reduce the effectiveness of drones, including the inverse relationship between resolution and speed and the lack of adequate labeled training data. This paper presents a two-step machine learning approach that analyzes low-fidelity and high-fidelity images in sequence, preserving efficiency as well as accuracy. Two data-generators are also used to minimize class imbalance in the high-fidelity dataset and to produce low-fidelity data that is representative of UA…

基于生成对抗网络的可控保真度极限图像压缩

Fidelity-Controllable Extreme Image Compression with Generative Adversarial Networks

本文提出一种基于GAN的极低码率图像压缩方法，通过两阶段训练和网络插值稳定训练并抑制噪声伪影，实现感知质量与保真度的可控权衡，实验和用户研究均验证其优于现有方法。

InvertAvatar：用于通用头部虚拟形象的增量式GAN反演

InvertAvatar: Incremental GAN Inversion for Generalized Head Avatars

提出增量式3D GAN反演框架InvertAvatar，利用多帧输入提升头部虚拟形象重建保真度，通过可动画3D GAN先验和神经纹理编码器增强表情控制与细节，实现一次性及少样本动画任务的最先进性能。

DPN-GAN：在生成对抗网络中引入周期性激活以实现高保真音频合成

DPN-GAN: Inducing Periodic Activations in Generative Adversarial Networks for High-Fidelity Audio Synthesis

本文提出DPN-GAN，通过周期性ReLU激活与可变形卷积增强生成器和判别器，在五个语音及音乐数据集上实现优于现有GAN的高保真、鲁棒音频合成。

MobileStyleGAN: A Lightweight Convolutional Neural Network for High-Fidelity Image Synthesis

In recent years, the use of Generative Adversarial Networks (GANs) has become very popular in generative image modeling. While style-based GAN architectures yield state-of-the-art results in high-fidelity image synthesis, computationally, they are highly complex. In our work, we focus on the performance optimization of style-based generative models. We analyze the most computationally hard parts of StyleGAN2, and propose changes in the generator network to make it possible to deploy style-based generative networks in the edge devices. We introduce MobileStyleGAN architecture, which has x3.5 fe…

Semi-supervised FusedGAN for Conditional Image Generation

We present FusedGAN, a deep network for conditional image synthesis with controllable sampling of diverse images. Fidelity, diversity and controllable sampling are the main quality measures of a good image generation model. Most existing models are insufficient in all three aspects. The FusedGAN can perform controllable sampling of diverse images with very high fidelity. We argue that controllability can be achieved by disentangling the generation process into various stages. In contrast to stacked GANs, where multiple stages of GANs are trained separately with full supervision of labeled inte…

用于单图像高保真3D头部重建的双编码器GAN反演

Dual Encoder GAN Inversion for High-Fidelity 3D Head Reconstruction from Single Images

本文提出基于PanoHead的双编码器GAN反演框架，通过三平面域拼接和遮挡感知判别器，实现单图像高保真3D头部重建与多视角生成。

Delving StyleGAN Inversion for Image Editing: A Foundation Latent Space Viewpoint

GAN inversion and editing via StyleGAN maps an input image into the embedding spaces ($\mathcal{W}$, $\mathcal{W^+}$, and $\mathcal{F}$) to simultaneously maintain image fidelity and meaningful manipulation. From latent space $\mathcal{W}$ to extended latent space $\mathcal{W^+}$ to feature space $\mathcal{F}$ in StyleGAN, the editability of GAN inversion decreases while its reconstruction quality increases. Recent GAN inversion methods typically explore $\mathcal{W^+}$ and $\mathcal{F}$ rather than $\mathcal{W}$ to improve reconstruction fidelity while maintaining editability. As $\mathcal{W^…

所见即所得GAN：为3D GAN中的高保真几何渲染每个像素

What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs

本文提出将神经体渲染扩展至原始2D图像分辨率，采用基于学习的采样器减少5倍深度样本，实现全像素渲染并解析高保真3D几何，在FFHQ和AFHQ上达到SOTA。

WaveGAN: Frequency-aware GAN for High-Fidelity Few-shot Image Generation

Existing few-shot image generation approaches typically employ fusion-based strategies, either on the image or the feature level, to produce new images. However, previous approaches struggle to synthesize high-frequency signals with fine details, deteriorating the synthesis quality. To address this, we propose WaveGAN, a frequency-aware model for few-shot image generation. Concretely, we disentangle encoded features into multiple frequency components and perform low-frequency skip connections to preserve outline and structural information. Then we alleviate the generator's struggles of synthes…

MeshGAN: Non-linear 3D Morphable Models of Faces

Generative Adversarial Networks (GANs) are currently the method of choice for generating visual data. Certain GAN architectures and training methods have demonstrated exceptional performance in generating realistic synthetic images (in particular, of human faces). However, for 3D object, GANs still fall short of the success they have had with images. One of the reasons is due to the fact that so far GANs have been applied as 3D convolutional architectures to discrete volumetric representations of 3D objects. In this paper, we propose the first intrinsic GANs architecture operating directly on …

Rethinking the Objectives of Vector-Quantized Tokenizers for Image Synthesis

Vector-Quantized (VQ-based) generative models usually consist of two basic components, i.e., VQ tokenizers and generative transformers. Prior research focuses on improving the reconstruction fidelity of VQ tokenizers but rarely examines how the improvement in reconstruction affects the generation ability of generative transformers. In this paper, we surprisingly find that improving the reconstruction fidelity of VQ tokenizers does not necessarily improve the generation. Instead, learning to compress semantic features within VQ tokenizers significantly improves generative transformers' ability …

StyleUV: Diverse and High-fidelity UV Map Generative Model

Reconstructing 3D human faces in the wild with the 3D Morphable Model (3DMM) has become popular in recent years. While most prior work focuses on estimating more robust and accurate geometry, relatively little attention has been paid to improving the quality of the texture model. Meanwhile, with the advent of Generative Adversarial Networks (GANs), there has been great progress in reconstructing realistic 2D images. Recent work demonstrates that GANs trained with abundant high-quality UV maps can produce high-fidelity textures superior to those produced by existing methods. However, acquiring …

SSD-Faster Net: A Hybrid Network for Industrial Defect Inspection

The quality of industrial components is critical to the production of special equipment such as robots. Defect inspection of these components is an efficient way to ensure quality. In this paper, we propose a hybrid network, SSD-Faster Net, for industrial defect inspection of rails, insulators, commutators etc. SSD-Faster Net is a two-stage network, including SSD for quickly locating defective blocks, and an improved Faster R-CNN for defect segmentation. For the former, we propose a novel slice localization mechanism to help SSD scan quickly. The second stage is based on improved Faster R-CNN,…

一种用于MRI到CT合成的GAN架构系统基准

A Systematic Benchmark of GAN Architectures for MRI-to-CT Synthesis

本文系统评估了十种GAN架构在MRI到CT合成中的性能，发现监督配对模型表现最佳，多区域训练提升结构鲁棒性，单区域训练则最大化像素精度。

GAN样本质量的无监督评估：引入TTJac分数

Unsupervised evaluation of GAN sample quality: Introducing the TTJac Score

本研究提出无数据评估指标TTJac分数，通过张量列逼近生成样本密度，衡量GAN合成图像保真度，并改善截断技巧下的保真度-可变性权衡，在多个数据集上验证有效性。

Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation

Data-hunger and data-imbalance are two major pitfalls in many deep learning approaches. For example, on highly optimized production lines, defective samples are hardly acquired while non-defective samples come almost for free. The defects however often seem to resemble each other, e.g., scratches on different products may only differ in a few characteristics. In this work, we introduce a framework, Defect Transfer GAN (DT-GAN), which learns to represent defect types independent of and across various background products and yet can apply defect-specific styles to generate realistic defective im…

Virtual Underwater Datasets for Autonomous Inspections

Underwater Vehicles have become more sophisticated, driven by the off-shore sector and the scientific community's rapid advancements in underwater operations. Notably, many underwater tasks, including the assessment of subsea infrastructure, are performed with the assistance of Autonomous Underwater Vehicles (AUVs). There have been recent breakthroughs in Artificial Intelligence (AI) and, notably, Deep Learning (DL) models and applications, which have widespread usage in a variety of fields, including aerial unmanned vehicles, autonomous car navigation, and other applications. However, they ar…

Image Processing Using Multi-Code GAN Prior

Despite the success of Generative Adversarial Networks (GANs) in image synthesis, applying trained GAN models to real image processing remains challenging. Previous methods typically invert a target image back to the latent space either by back-propagation or by learning an additional encoder. However, the reconstructions from both of the methods are far from ideal. In this work, we propose a novel approach, called mGANprior, to incorporate the well-trained GANs as effective prior to a variety of image processing tasks. In particular, we employ multiple latent codes to generate multiple featur…

High-Fidelity Synthesis with Disentangled Representation

Learning disentangled representation of data without supervision is an important step towards improving the interpretability of generative models. Despite recent advances in disentangled representation learning, existing approaches often suffer from the trade-off between representation learning and generation performance i.e. improving generation quality sacrifices disentanglement performance). We propose an Information-Distillation Generative Adversarial Network (ID-GAN), a simple yet generic framework that easily incorporates the existing state-of-the-art models for both disentanglement lear…

Panini-Net: GAN Prior Based Degradation-Aware Feature Interpolation for Face Restoration

Emerging high-quality face restoration (FR) methods often utilize pre-trained GAN models (\textit{i.e.}, StyleGAN2) as GAN Prior. However, these methods usually struggle to balance realness and fidelity when facing various degradation levels. Besides, there is still a noticeable visual quality gap compared with pre-trained GAN models. In this paper, we propose a novel GAN Prior based degradation-aware feature interpolation network, dubbed Panini-Net, for FR tasks by explicitly learning the abstract representations to distinguish various degradations. Specifically, an unsupervised degradation r…

Interpreting the Latent Space of GANs for Semantic Face Editing

Despite the recent advance of Generative Adversarial Networks (GANs) in high-fidelity image synthesis, there lacks enough understanding of how GANs are able to map a latent code sampled from a random distribution to a photo-realistic image. Previous work assumes the latent space learned by GANs follows a distributed representation but observes the vector arithmetic phenomenon. In this work, we propose a novel framework, called InterFaceGAN, for semantic face editing by interpreting the latent semantics learned by GANs. In this framework, we conduct a detailed study on how different semantics a…

dc-GAN：用于从单个合成图像中恢复原始人脸的双重条件GAN

dc-GAN: Dual-Conditioned GAN for Face Demorphing From a Single Morph

本文提出dc-GAN（双重条件GAN），通过以合成图像及其嵌入为条件，克服现有去合成技术的身份依赖和合成复制问题，实现高保真原始图像重建，适用于参考和无参考场景。

Label Geometry Aware Discriminator for Conditional Generative Networks

Multi-domain image-to-image translation with conditional Generative Adversarial Networks (GANs) can generate highly photo realistic images with desired target classes, yet these synthetic images have not always been helpful to improve downstream supervised tasks such as image classification. Improving downstream tasks with synthetic examples requires generating images with high fidelity to the unknown conditional distribution of the target class, which many labeled conditional GANs attempt to achieve by adding soft-max cross-entropy loss based auxiliary classifier in the discriminator. As rece…

超越单一模式：用于多样化医学数据生成的GAN集成

Beyond a Single Mode: GAN Ensembles for Diverse Medical Data Generation

本文提出了一种基于GAN集成的方法，用于生成多样化且高效的医学影像数据，以提升下游任务的性能。

效率与不妥协：CLIP辅助的文本到图像GAN及其增强的多样性

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity

本文提出SCAD模型，通过CLIP辅助和两个专用判别器结合SANs，在显著降低训练成本的同时提升文本到图像GAN的生成多样性与保真度，并引入PPD指标进行定量评估。

Model Extraction and Defenses on Generative Adversarial Networks

Model extraction attacks aim to duplicate a machine learning model through query access to a target model. Early studies mainly focus on discriminative models. Despite the success, model extraction attacks against generative models are less well explored. In this paper, we systematically study the feasibility of model extraction attacks against generative adversarial networks (GANs). Specifically, we first define accuracy and fidelity on model extraction attacks against GANs. Then we study model extraction attacks against GANs from the perspective of accuracy extraction and fidelity extraction…

BigGAN-based Bayesian reconstruction of natural images from human brain activity

In the visual decoding domain, visually reconstructing presented images given the corresponding human brain activity monitored by functional magnetic resonance imaging (fMRI) is difficult, especially when reconstructing viewed natural images. Visual reconstruction is a conditional image generation on fMRI data and thus generative adversarial network (GAN) for natural image generation is recently introduced for this task. Although GAN-based methods have greatly improved, the fidelity and naturalness of reconstruction are still unsatisfactory due to the small number of fMRI data samples and the …

Generating Pixel Art Character Sprites using GANs

Iterating on creating pixel art character sprite sheets is essential to the game development process. However, it can take a lot of effort until the final versions containing different poses and animation clips are achieved. This paper investigates using conditional generative adversarial networks to aid the designers in creating such sprite sheets. We propose an architecture based on Pix2Pix to generate images of characters facing a target side (e.g., right) given sprites of them in a source pose (e.g., front). Experiments with small pixel art datasets yielded promising results, resulting in …

基于小波的扩散GAN用于图像超分辨率

A Wavelet Diffusion GAN for Image Super-Resolution

本文提出基于小波的扩散GAN方案，通过减少反向扩散步骤和利用小波变换进行降维，提升图像超分辨率的训练和推理效率。实验验证在CelebA-HQ数据集上优于现有方法，实现高保真输出。

LFS-GAN：终身少样本图像生成

LFS-GAN: Lifelong Few-Shot Image Generation

提出LFS-GAN框架，首次解决终身少样本图像生成任务，通过可学习因子化张量调制器和模态寻求损失，实现高保真、多样化图像生成且无遗忘和模式崩溃，性能超越现有方法。

LSAP: 重新思考GAN潜在空间中的反向保真度、感知性和可编辑性

LSAP: Rethinking Inversion Fidelity, Perception and Editability in GAN Latent Space

LSAP通过引入归一化风格空间和NSCD度量，提升GAN潜在空间中反向保真度、感知性和可编辑性的对齐性能。