尽管具有生成对抗网络（GAN）的图像到图像（I2I）翻译的显着进步，但使用单对生成器和歧视器将图像有效地转换为多个目标域中的一组不同图像仍然具有挑战性。现有的I2i翻译方法采用多个针对不同域的特定于域的内容编码，其中每个特定于域的内容编码器仅经过来自同一域的图像的训练。然而，我们认为应从所有域之间的图像中学到内容（域变相）特征。因此，现有方案的每个特定于域的内容编码器都无法有效提取域不变特征。为了解决这个问题，我们提出了一个灵活而通用的Sologan模型，用于在多个域之间具有未配对数据的多模式I2I翻译。与现有方法相反，Solgan算法使用具有附加辅助分类器的单个投影鉴别器，并为所有域共享编码器和生成器。因此，可以使用来自所有域的图像有效地训练Solgan，从而可以有效提取域 - 不变性内容表示。在多个数据集中，针对多个同行和sologan的变体的定性和定量结果证明了该方法的优点，尤其是对于挑战i2i翻译数据集的挑战，即涉及极端形状变化的数据集或在翻译后保持复杂的背景，需要保持复杂的背景。此外，我们通过消融研究证明了Sogan中每个成分的贡献。

Unsupervised image-to-image translation is an important and challenging problem in computer vision. Given an image in the source domain, the goal is to learn the conditional distribution of corresponding images in the target domain, without seeing any examples of corresponding image pairs. While this conditional distribution is inherently multimodal, existing approaches make an overly simplified assumption, modeling it as a deterministic one-to-one mapping. As a result, they fail to generate diverse outputs from a given source domain image. To address this limitation, we propose a Multimodal Unsupervised Image-to-image Translation (MUNIT) framework. We assume that the image representation can be decomposed into a content code that is domain-invariant, and a style code that captures domain-specific properties. To translate an image to another domain, we recombine its content code with a random style code sampled from the style space of the target domain. We analyze the proposed framework and establish several theoretical results. Extensive experiments with comparisons to state-of-the-art approaches further demonstrate the advantage of the proposed framework. Moreover, our framework allows users to control the style of translation outputs by providing an example style image. Code and pretrained models are available at https://github.com/nvlabs/MUNIT.

Many image-to-image translation problems are ambiguous, as a single input image may correspond to multiple possible outputs. In this work, we aim to model a distribution of possible outputs in a conditional generative modeling setting. The ambiguity of the mapping is distilled in a low-dimensional latent vector, which can be randomly sampled at test time. A generator learns to map the given input, combined with this latent code, to the output. We explicitly encourage the connection between output and the latent code to be invertible. This helps prevent a many-to-one mapping from the latent code to the output during training, also known as the problem of mode collapse, and produces more diverse results. We explore several variants of this approach by employing different training objectives, network architectures, and methods of injecting the latent code. Our proposed method encourages bijective consistency between the latent encoding and output modes. We present a systematic comparison of our method and other variants on both perceptual realism and diversity.

当前的图像到图像翻译方法通过条件生成模型来制定任务，从而仅学习重塑或区域变化，因为条件上下文提供的丰富结构信息受到了约束。在这项工作中，我们建议将矢量量化技术引入图像到图像翻译框架。矢量量化的内容表示不仅可以促进翻译，还可以促进不同域之间共享的无条件分布。同时，加上散布的样式表示，提出的方法进一步使图像扩展能力具有灵活性，并在内域内和域间具有灵活性。定性和定量实验表明，我们的框架与最先进的图像到图像到图像翻译和图像扩展方法的性能可比。与单个任务的方法相比，所提出的方法是统一的框架，释放了组合图像到图像翻译，无条件生成和图像扩展的应用程序。例如，它为图像生成和扩展提供了样式的可变性，并为图像到图像翻译提供了进一步的扩展功能。

凝视和头部姿势估计模型的鲁棒性高度取决于标记的数据量。最近，生成建模在生成照片现实图像方面表现出了出色的结果，这可以减轻对标记数据的需求。但是，在新领域采用这种生成模型，同时保持其对不同图像属性的细粒度控制的能力，例如，凝视和头部姿势方向，是一个挑战性的问题。本文提出了Cuda-GHR，这是一种无监督的域适应框架，可以对凝视和头部姿势方向进行细粒度的控制，同时保留该人的外观相关因素。我们的框架同时学会了通过利用富含标签的源域和未标记的目标域来适应新的域和删除图像属性，例如外观，凝视方向和头部方向。基准测试数据集的广泛实验表明，所提出的方法在定量和定性评估上都可以胜过最先进的技术。此外，我们表明目标域中生成的图像标签对有效地传递知识并提高下游任务的性能。

Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains. Since there exists an infinite set of joint distributions that can arrive the given marginal distributions, one could infer nothing about the joint distribution from the marginal distributions without additional assumptions. To address the problem, we make a shared-latent space assumption and propose an unsupervised image-to-image translation framework based on Coupled GANs. We compare the proposed framework with competing approaches and present high quality image translation results on various challenging unsupervised image translation tasks, including street scene image translation, animal image translation, and face image translation. We also apply the proposed framework to domain adaptation and achieve state-of-the-art performance on benchmark datasets. Code and additional results are available in https://github.com/mingyuliutw/unit.

Figure 1. Multi-domain image-to-image translation results on the CelebA dataset via transferring knowledge learned from the RaFD dataset. The first and sixth columns show input images while the remaining columns are images generated by StarGAN. Note that the images are generated by a single generator network, and facial expression labels such as angry, happy, and fearful are from RaFD, not CelebA.

Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks. An appealing alternative is to render synthetic data where ground-truth annotations are generated automatically. Unfortunately, models trained purely on rendered images often fail to generalize to real images. To address this shortcoming, prior work introduced unsupervised domain adaptation algorithms that attempt to map representations between the two domains or learn to extract features that are domain-invariant. In this work, we present a new approach that learns, in an unsupervised manner, a transformation in the pixel space from one domain to the other. Our generative adversarial network (GAN)-based model adapts source-domain images to appear as if drawn from the target domain. Our approach not only produces plausible samples, but also outperforms the state-of-the-art on a number of unsupervised domain adaptation scenarios by large margins. Finally, we demonstrate that the adaptation process generalizes to object classes unseen during training.

生成的对抗网络（GANS）最近引入了执行图像到图像翻译的有效方法。这些模型可以应用于图像到图像到图像转换中的各种域而不改变任何参数。在本文中，我们调查并分析了八个图像到图像生成的对策网络：PIX2PX，Cyclegan，Cogan，Stargan，Munit，Stargan2，Da-Gan，以及自我关注GaN。这些模型中的每一个都呈现了最先进的结果，并引入了构建图像到图像的新技术。除了对模型的调查外，我们还调查了他们接受培训的18个数据集，并在其上进行了评估的9个指标。最后，我们在常见的一组指标和数据集中呈现6种这些模型的受控实验的结果。结果混合并显示，在某些数据集，任务和指标上，某些型号优于其他型号。本文的最后一部分讨论了这些结果并建立了未来研究领域。由于研究人员继续创新新的图像到图像GAN，因此他们非常重要地了解现有方法，数据集和指标。本文提供了全面的概述和讨论，以帮助构建此基础。

恶劣的天气图像翻译属于无监督的图像到图像（I2i）翻译任务，旨在将不利条件领域（例如，雨夜）转移到标准领域（例如，日期）。这是一个具有挑战性的任务，因为来自不利域的图像具有一些伪影和信息不足。最近，许多采用生成的对抗性网络（GANS）的研究在I2I翻译中取得了显着的成功，但仍然有限制将它们应用于恶劣天气增强。基于双向循环 - 一致性损耗的对称架构被采用作为无监督域传输方法的标准框架。但是，如果两个域具有不平衡信息，它可能会导致较差的转换结果。为了解决这个问题，我们提出了一种新的GaN模型，即Au-GaN，它具有不对称的域翻译的非对称架构。我们仅在普通域生成器（即雨夜 - >日）中插入建议的功能传输网络（$ {T} $ - 网），以增强不利域图像的编码特征。此外，我们介绍了对编码特征的解剖学的非对称特征匹配。最后，我们提出了不确定感知的周期 - 一致性损失，以解决循环重建图像的区域不确定性。我们通过与最先进的模型进行定性和定量比较来证明我们的方法的有效性。代码在https://github.com/jgkwak95/au-g中提供。

In this paper, we investigate a challenging unsupervised domain adaptation setting -unsupervised model adaptation. We aim to explore how to rely only on unlabeled target data to improve performance of an existing source prediction model on the target domain, since labeled source data may not be available in some real-world scenarios due to data privacy issues. For this purpose, we propose a new framework, which is referred to as collaborative class conditional generative adversarial net to bypass the dependence on the source data. Specifically, the prediction model is to be improved through generated target-style data, which provides more accurate guidance for the generator. As a result, the generator and the prediction model can collaborate with each other without source data. Furthermore, due to the lack of supervision from source data, we propose a weight constraint that encourages similarity to the source model. A clustering-based regularization is also introduced to produce more discriminative features in the target domain. Compared to conventional domain adaptation methods, our model achieves superior performance on multiple adaptation tasks with only unlabeled target data, which verifies its effectiveness in this challenging setting.

We propose a general framework for unsupervised domain adaptation, which allows deep neural networks trained on a source domain to be tested on a different target domain without requiring any training annotations in the target domain. This is achieved by adding extra networks and losses that help regularize the features extracted by the backbone encoder network. To this end we propose the novel use of the recently proposed unpaired image-toimage translation framework to constrain the features extracted by the encoder network. Specifically, we require that the features extracted are able to reconstruct the images in both domains. In addition we require that the distribution of features extracted from images in the two domains are indistinguishable. Many recent works can be seen as specific cases of our general framework. We apply our method for domain adaptation between MNIST, USPS, and SVHN datasets, and Amazon, Webcam and DSLR Office datasets in classification tasks, and also between GTA5 and Cityscapes datasets for a segmentation task. We demonstrate state of the art performance on each of these datasets.

Current domain adaptation methods for face anti-spoofing leverage labeled source domain data and unlabeled target domain data to obtain a promising generalizable decision boundary. However, it is usually difficult for these methods to achieve a perfect domain-invariant liveness feature disentanglement, which may degrade the final classification performance by domain differences in illumination, face category, spoof type, etc. In this work, we tackle cross-scenario face anti-spoofing by proposing a novel domain adaptation method called cyclically disentangled feature translation network (CDFTN). Specifically, CDFTN generates pseudo-labeled samples that possess: 1) source domain-invariant liveness features and 2) target domain-specific content features, which are disentangled through domain adversarial training. A robust classifier is trained based on the synthetic pseudo-labeled images under the supervision of source domain labels. We further extend CDFTN for multi-target domain adaptation by leveraging data from more unlabeled target domains. Extensive experiments on several public datasets demonstrate that our proposed approach significantly outperforms the state of the art.

Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-level and low-level domain shifts. Recent work has shown that generative adversarial networks combined with cycle-consistency constraints are surprisingly effective at mapping images between domains, even without the use of aligned image pairs. We propose a novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model. CyCADA adapts representations at both the pixel-level and feature-level, enforces cycle-consistency while leveraging a task loss, and does not require aligned pairs. Our model can be applied in a variety of visual recognition and prediction settings. We show new state-of-the-art results across multiple adaptation tasks, including digit classification and semantic segmentation of road scenes demonstrating transfer from synthetic to real world domains.

While humans easily recognize relations between data from different domains without any supervision, learning to automatically discover them is in general very challenging and needs many ground-truth pairs that illustrate the relations. To avoid costly pairing, we address the task of discovering cross-domain relations given unpaired data. We propose a method based on generative adversarial networks that learns to discover relations between different domains (DiscoGAN). Using the discovered relations, our proposed network successfully transfers style from one domain to another while preserving key attributes such as orientation and face identity.

建筑摄影是一种摄影类型，重点是捕获前景中带有戏剧性照明的建筑物或结构。受图像到图像翻译方法的成功启发，我们旨在为建筑照片执行风格转移。但是，建筑摄影中的特殊构图对这类照片中的样式转移构成了巨大挑战。现有的神经风格转移方法将建筑图像视为单个实体，它将产生与原始建筑的几何特征，产生不切实际的照明，错误的颜色演绎以及可视化伪影，例如幽灵，外观失真或颜色不匹配。在本文中，我们专门针对建筑摄影的神经风格转移方法。我们的方法解决了两个分支神经网络中建筑照片中前景和背景的组成，该神经网络分别考虑了前景和背景的样式转移。我们的方法包括一个分割模块，基于学习的图像到图像翻译模块和图像混合优化模块。我们使用了一天中不同的魔术时代捕获的不受限制的户外建筑照片的新数据集培训了图像到图像的翻译神经网络，利用其他语义信息，以更好地匹配和几何形状保存。我们的实验表明，我们的方法可以在前景和背景上产生逼真的照明和颜色演绎，并且在定量和定性上都优于一般图像到图像转换和任意样式转移基线。我们的代码和数据可在https://github.com/hkust-vgd/architectural_style_transfer上获得。

在低灯条件下捕获的图像遭受低可视性和各种成像伪影，例如真实噪音。现有的监督启示算法需要大量的像素对齐的训练图像对，这很难在实践中准备。虽然弱监督或无人监督的方法可以缓解这些挑战，但不使用配对的训练图像，由于缺乏相应的监督，一些现实世界的文物不可避免地被错误地放大。在本文中，而不是使用完美的对齐图像进行培训，我们创造性地使用未对准的现实世界图像作为指导，这很容易收集。具体地，我们提出了一个交叉图像解剖线程（CIDN），以分别提取来自低/常光图像的交叉图像亮度和图像特定内容特征。基于此，CIDN可以同时校正特征域中的亮度和抑制图像伪像，其在很大程度上将鲁棒性增加到像素偏移。此外，我们收集了一个新的低光图像增强数据集，包括具有现实世界腐败的未对准培训图像。实验结果表明，我们的模型在新建议的数据集和其他流行的低光数据集中实现了最先进的表演。