计算机视觉中有意义的不确定性量化需要有关语义信息的推理 - 例如，照片中的人的头发颜色或街上汽车的位置。为此，最近在生成建模方面的突破使我们能够在分离的潜在空间中代表语义信息，但是在语义潜在变量上提供不确定性仍然具有挑战性。在这项工作中，我们提供了原则上的不确定性间隔，这些间隔可保证为任何潜在的生成模型包含真正的语义因素。该方法执行以下操作：（1）它使用分位数回归来输出潜在空间中每个元素的启发式不确定性间隔（2）校准了这些不确定性，以使它们包含新的，看不见的输入的潜在值。然后可以通过发电机传播这些校准间隔的终点，以为每个语义因素产生可解释的不确定性可视化。该技术可靠地传达了语义上有意义的，有原则和实例自适应的不确定性，例如图像超分辨率和图像完成。

现在通常用于高风险设置，如医疗诊断，如医疗诊断，那么需要不确定量化，以避免后续模型失败。无分发的不确定性量化（无分布UQ）是用户友好的范式，用于为这种预测创建统计上严格的置信区间/集合。批判性地，间隔/集合有效而不进行分布假设或模型假设，即使具有最多许多DataPoints也具有显式保证。此外，它们适应输入的难度;当输入示例很困难时，不确定性间隔/集很大，信号传达模型可能是错误的。在没有多大的工作和没有再培训的情况下，可以在任何潜在的算法（例如神经网络）上使用无分​​发方法，以产生置信度集，以便包含用户指定概率，例如90％。实际上，这些方法易于理解和一般，应用于计算机视觉，自然语言处理，深度加强学习等领域出现的许多现代预测问题。这种实践介绍是针对对无需统计学家的免费UQ的实际实施感兴趣的读者。我们通过实际的理论和无分发UQ的应用领导读者，从保形预测开始，并使无关的任何风险的分布控制，如虚假发现率，假阳性分布检测，等等。我们将包括Python中的许多解释性插图，示例和代码样本，具有Pytorch语法。目标是提供读者对无分配UQ的工作理解，使它们能够将置信间隔放在算法上，其中包含一个自包含的文档。

Estimating uncertainty in image-to-image networks is an important task, particularly as such networks are being increasingly deployed in the biological and medical imaging realms. In this paper, we introduce a new approach to this problem based on masking. Given an existing image-to-image network, our approach computes a mask such that the distance between the masked reconstructed image and the masked true image is guaranteed to be less than a specified threshold, with high probability. The mask thus identifies the more certain regions of the reconstructed image. Our approach is agnostic to the underlying image-to-image network, and only requires triples of the input (degraded), reconstructed and true images for training. Furthermore, our method is agnostic to the distance metric used. As a result, one can use $L_p$-style distances or perceptual distances like LPIPS, which contrasts with interval-based approaches to uncertainty. Our theoretical guarantees derive from a conformal calibration procedure. We evaluate our mask-based approach to uncertainty on image colorization, image completion, and super-resolution tasks, demonstrating high quality performance on each.

Figure 1. The proposed pixel2style2pixel framework can be used to solve a wide variety of image-to-image translation tasks. Here we show results of pSp on StyleGAN inversion, multi-modal conditional image synthesis, facial frontalization, inpainting and super-resolution.

The primary aim of single-image super-resolution is to construct a high-resolution (HR) image from a corresponding low-resolution (LR) input. In previous approaches, which have generally been supervised, the training objective typically measures a pixel-wise average distance between the super-resolved (SR) and HR images. Optimizing such metrics often leads to blurring, especially in high variance (detailed) regions. We propose an alternative formulation of the super-resolution problem based on creating realistic SR images that downscale correctly. We present a novel super-resolution algorithm addressing this problem, PULSE (Photo Upsampling via Latent Space Exploration), which generates high-resolution, realistic images at resolutions previously unseen in the literature. It accomplishes this in an entirely self-supervised fashion and is not confined to a specific degradation operator used during training, unlike previous methods (which require training on databases of LR-HR image pairs for supervised learning). Instead of starting with the LR image and slowly adding detail, PULSE traverses the high-resolution natural image manifold, searching for images that downscale to the original LR image. This is formalized through the "downscaling loss," which guides exploration through the latent space of a generative model. By leveraging properties of high-dimensional Gaussians, we restrict the search space to guarantee that our outputs are realistic. PULSE thereby generates super-resolved images that both are realistic and downscale correctly. We show extensive experimental results demonstrating the efficacy of our approach in the domain of face super-resolution (also known as face hallucination). We also present a discussion of the limitations and biases of the method as currently implemented with an accompanying model card with relevant metrics. Our method outperforms state-of-the-art methods in perceptual quality at higher resolutions and scale factors than previously pos-sible.

Our goal with this survey is to provide an overview of the state of the art deep learning technologies for face generation and editing. We will cover popular latest architectures and discuss key ideas that make them work, such as inversion, latent representation, loss functions, training procedures, editing methods, and cross domain style transfer. We particularly focus on GAN-based architectures that have culminated in the StyleGAN approaches, which allow generation of high-quality face images and offer rich interfaces for controllable semantics editing and preserving photo quality. We aim to provide an entry point into the field for readers that have basic knowledge about the field of deep learning and are looking for an accessible introduction and overview.

机器学习模型通常培训端到端和监督设置，使用配对（输入，输出）数据。示例包括最近的超分辨率方法，用于在（低分辨率，高分辨率）图像上培训。然而，这些端到端的方法每当输入中存在分布偏移时需要重新训练（例如，夜间图像VS日光）或相关的潜在变量（例如，相机模糊或手动运动）。在这项工作中，我们利用最先进的（SOTA）生成模型（这里是Stylegan2）来构建强大的图像前提，这使得贝叶斯定理应用于许多下游重建任务。我们的方法是通过生成模型（BRGM）的贝叶斯重建，使用单个预先训练的发生器模型来解决不同的图像恢复任务，即超级分辨率和绘画，通过与不同的前向腐败模型相结合。我们将发电机模型的重量保持固定，并通过估计产生重建图像的输入潜在的跳过载体来重建图像来估计图像。我们进一步使用变分推理来近似潜伏向量的后部分布，我们对多种解决方案进行采样。我们在三个大型和多样化的数据集中展示了BRGM：（i）来自Flick的60,000个图像面向高质量的数据集（II）来自MIMIC III的高质量数据集（II）240,000胸X射线，（III）的组合收集5脑MRI数据集，具有7,329个扫描。在所有三个数据集和没有任何DataSet特定的HyperParameter调整，我们的简单方法会在超级分辨率和绘画上对当前的特定任务最先进的方法产生性能竞争力，同时更加稳定，而不需要任何培训。我们的源代码和预先训练的型号可在线获取：https://razvanmarinescu.github.io/brgm/。

Recent 3D-aware GANs rely on volumetric rendering techniques to disentangle the pose and appearance of objects, de facto generating entire 3D volumes rather than single-view 2D images from a latent code. Complex image editing tasks can be performed in standard 2D-based GANs (e.g., StyleGAN models) as manipulation of latent dimensions. However, to the best of our knowledge, similar properties have only been partially explored for 3D-aware GAN models. This work aims to fill this gap by showing the limitations of existing methods and proposing LatentSwap3D, a model-agnostic approach designed to enable attribute editing in the latent space of pre-trained 3D-aware GANs. We first identify the most relevant dimensions in the latent space of the model controlling the targeted attribute by relying on the feature importance ranking of a random forest classifier. Then, to apply the transformation, we swap the top-K most relevant latent dimensions of the image being edited with an image exhibiting the desired attribute. Despite its simplicity, LatentSwap3D provides remarkable semantic edits in a disentangled manner and outperforms alternative approaches both qualitatively and quantitatively. We demonstrate our semantic edit approach on various 3D-aware generative models such as pi-GAN, GIRAFFE, StyleSDF, MVCGAN, EG3D and VolumeGAN, and on diverse datasets, such as FFHQ, AFHQ, Cats, MetFaces, and CompCars. The project page can be found: \url{https://enisimsar.github.io/latentswap3d/}.

We develop a method to generate predictive regions that cover a multivariate response variable with a user-specified probability. Our work is composed of two components. First, we use a deep generative model to learn a representation of the response that has a unimodal distribution. Existing multiple-output quantile regression approaches are effective in such cases, so we apply them on the learned representation, and then transform the solution to the original space of the response. This process results in a flexible and informative region that can have an arbitrary shape, a property that existing methods lack. Second, we propose an extension of conformal prediction to the multivariate response setting that modifies any method to return sets with a pre-specified coverage level. The desired coverage is theoretically guaranteed in the finite-sample case for any distribution. Experiments conducted on both real and synthetic data show that our method constructs regions that are significantly smaller compared to existing techniques.

edu.hk (a) Image Reconstruction (b) Image Colorization (c) Image Super-Resolution (d) Image Denoising (e) Image Inpainting (f) Semantic Manipulation Figure 1: Multi-code GAN prior facilitates many image processing applications using the reconstruction from fixed PGGAN [23] models.

We introduce a new method for diverse foreground generation with explicit control over various factors. Existing image inpainting based foreground generation methods often struggle to generate diverse results and rarely allow users to explicitly control specific factors of variation (e.g., varying the facial identity or expression for face inpainting results). We leverage contrastive learning with latent codes to generate diverse foreground results for the same masked input. Specifically, we define two sets of latent codes, where one controls a pre-defined factor (``known''), and the other controls the remaining factors (``unknown''). The sampled latent codes from the two sets jointly bi-modulate the convolution kernels to guide the generator to synthesize diverse results. Experiments demonstrate the superiority of our method over state-of-the-arts in result diversity and generation controllability.

Stone" "Mohawk hairstyle" "Without makeup" "Cute cat" "Lion" "Gothic church" * Equal contribution, ordered alphabetically. Code and video are available on https://github.com/orpatashnik/StyleCLIP

We explore and analyze the latent style space of Style-GAN2, a state-of-the-art architecture for image generation, using models pretrained on several different datasets. We first show that StyleSpace, the space of channel-wise style parameters, is significantly more disentangled than the other intermediate latent spaces explored by previous works. Next, we describe a method for discovering a large collection of style channels, each of which is shown to control a distinct visual attribute in a highly localized and disentangled manner. Third, we propose a simple method for identifying style channels that control a specific attribute, using a pretrained classifier or a small number of example images. Manipulation of visual attributes via these StyleSpace controls is shown to be better disentangled than via those proposed in previous works. To show this, we make use of a newly proposed Attribute Dependency metric. Finally, we demonstrate the applicability of StyleSpace controls to the manipulation of real images. Our findings pave the way to semantically meaningful and well-disentangled image manipulations via simple and intuitive interfaces.

生成照片 - 现实图像，语义编辑和表示学习是高分辨率生成模型的许多潜在应用中的一些。最近在GAN的进展将它们建立为这些任务的绝佳选择。但是，由于它们不提供推理模型，因此使用GaN潜在空间无法在实际图像上完成诸如分类的图像编辑或下游任务。尽管培训了训练推理模型或设计了一种迭代方法来颠覆训练有素的发生器，但之前的方法是数据集（例如人类脸部图像）和架构（例如样式）。这些方法是非延伸到新型数据集或架构的。我们提出了一般框架，该框架是不可知的架构和数据集。我们的主要识别是，通过培训推断和生成模型在一起，我们允许它们彼此适应并收敛到更好的质量模型。我们的\ textbf {invang}，可逆GaN的简短，成功将真实图像嵌入到高质量的生成模型的潜在空间。这使我们能够执行图像修复，合并，插值和在线数据增强。我们展示了广泛的定性和定量实验。

Privacy of machine learning models is one of the remaining challenges that hinder the broad adoption of Artificial Intelligent (AI). This paper considers this problem in the context of image datasets containing faces. Anonymization of such datasets is becoming increasingly important due to their central role in the training of autonomous cars, for example, and the vast amount of data generated by surveillance systems. While most prior work de-identifies facial images by modifying identity features in pixel space, we instead project the image onto the latent space of a Generative Adversarial Network (GAN) model, find the features that provide the biggest identity disentanglement, and then manipulate these features in latent space, pixel space, or both. The main contribution of the paper is the design of a feature-preserving anonymization framework, StyleID, which protects the individuals' identity, while preserving as many characteristics of the original faces in the image dataset as possible. As part of the contribution, we present a novel disentanglement metric, three complementing disentanglement methods, and new insights into identity disentanglement. StyleID provides tunable privacy, has low computational complexity, and is shown to outperform current state-of-the-art solutions.

与传统的头像创建管道相反，这是一个昂贵的过程，现代生成方法直接从照片中学习数据分布，而艺术的状态现在可以产生高度的照片现实图像。尽管大量作品试图扩展无条件的生成模型并达到一定程度的可控性，但要确保多视图一致性，尤其是在大型姿势中，仍然具有挑战性。在这项工作中，我们提出了一个3D肖像生成网络，该网络可产生3D一致的肖像，同时根据有关姿势，身份，表达和照明的语义参数可控。生成网络使用神经场景表示在3D中建模肖像，其生成以支持明确控制的参数面模型为指导。尽管可以通过将图像与部分不同的属性进行对比，但可以进一步增强潜在的分离，但在非面积区域（例如，在动画表达式）时，仍然存在明显的不一致。我们通过提出一种体积混合策略来解决此问题，在该策略中，我们通过将动态和静态辐射场融合在一起，形成一个复合输出，并从共同学习的语义场中分割了两个部分。我们的方法在广泛的实验中优于先前的艺术，在自由视点中观看时，在自然照明中产生了逼真的肖像。所提出的方法还证明了真实图像以及室外卡通面孔的概括能力，在实际应用中显示出巨大的希望。其他视频结果和代码将在项目网页上提供。

图像介入寻求一种语义一致的方法，以根据其未掩盖的内容来恢复损坏的图像。以前的方法通常将训练有素的甘恩重复使用，然后在产生逼真的斑块中用于缺少GAN反转的孔。然而，在这些算法中对硬约束的无知可能会产生gan倒置和图像插入之间的差距。在解决这个问题的情况下，我们在本文中设计了一个新颖的GAN反转模型，用于图像插入，称为Interverfill，主要由带有预调制模块的编码器和具有F＆W+潜在空间的GAN生成器组成。在编码器中，预调制网络利用多尺度结构将更多的歧视语义编码为样式向量。为了弥合GAN倒置和图像插入之间的缝隙，提出了F＆W+潜在空间以消除巨大的颜色差异和语义不一致。为了重建忠实和逼真的图像，一个简单而有效的软上升平均潜在模块旨在捕获更多样化的内域模式，以合成大型腐败的高保真质地。在包括Ploce2，Celeba-HQ，Metfaces和Scenery在内的四个具有挑战性的数据集上进行的全面实验表明，我们的Intervill效果优于定性和定量的高级方法，并支持室外图像的完成。

我们介绍了学习然后测试，校准机器学习模型的框架，使其预测满足明确的，有限样本统计保证，无论底层模型如何和（未知）数据生成分布。框架地址，以及在其他示例中，在多标签分类中的错误发现速率控制，在实例分割中交叉联盟控制，以及同时控制分类或回归中的异常检测和置信度覆盖的类型误差。为实现这一目标，我们解决了一个关键的技术挑战：控制不一定单调的任意风险。我们的主要洞察力是将风险控制问题重新构建为多个假设检测，使技术和数学论据不同于先前文献中的技术。我们使用我们的框架为多个核心机器学习任务提供新的校准方法，在计算机视觉中具有详细的工作示例。

The introduction of high-quality image generation models, particularly the StyleGAN family, provides a powerful tool to synthesize and manipulate images. However, existing models are built upon high-quality (HQ) data as desired outputs, making them unfit for in-the-wild low-quality (LQ) images, which are common inputs for manipulation. In this work, we bridge this gap by proposing a novel GAN structure that allows for generating images with controllable quality. The network can synthesize various image degradation and restore the sharp image via a quality control code. Our proposed QC-StyleGAN can directly edit LQ images without altering their quality by applying GAN inversion and manipulation techniques. It also provides for free an image restoration solution that can handle various degradations, including noise, blur, compression artifacts, and their mixtures. Finally, we demonstrate numerous other applications such as image degradation synthesis, transfer, and interpolation.

反转生成对抗网络（GAN）可以使用预训练的发电机来促进广泛的图像编辑任务。现有方法通常采用gan的潜在空间作为反转空间，但观察到空间细节的恢复不足。在这项工作中，我们建议涉及发电机的填充空间，以通过空间信息补充潜在空间。具体来说，我们替换具有某些实例感知系数的卷积层中使用的恒定填充（例如，通常为零）。通过这种方式，可以适当地适当地适应了预训练模型中假定的归纳偏差以适合每个单独的图像。通过学习精心设计的编码器，我们设法在定性和定量上提高了反演质量，超过了现有的替代方案。然后，我们证明了这样的空间扩展几乎不会影响天然甘纳的歧管，因此我们仍然可以重复使用甘斯（Gans）对各种下游应用学到的先验知识。除了在先前的艺术中探讨的编辑任务外，我们的方法还可以进行更灵活的图像操纵，例如对面部轮廓和面部细节的单独控制，并启用一种新颖的编辑方式，用户可以高效地自定义自己的操作。