Stylegan的成功使得在合成和真实图像上启用了前所未有的语义编辑能力。然而，这种编辑操作要么是使用人类指导的语义监督或描述的培训。在另一个开发中，剪辑架构已被互联网级图像和文本配对培训，并且已被示出在几个零拍摄学习设置中有用。在这项工作中，我们调查了如何有效地链接样式登录和剪辑的预训练潜空间，这反过来允许我们从Stylegan，查找和命名有意义的编辑操作自动提取语义标记的编辑方向，而无需任何额外的人类指导。从技术上讲，我们提出了两块新颖的建筑块;一个用于查找有趣的夹子方向，一个用于在CLIP潜在空间中标记任意方向。安装程序不假设任何预定的标签，因此我们不需要任何其他监督文本/属性来构建编辑框架。我们评估所提出的方法的有效性，并证明了解标记标记的样式编辑方向的提取确实可能，并揭示了有趣和非琐碎的编辑方向。

我们为一个拍摄域适应提供了一种新方法。我们方法的输入是训练的GaN，其可以在域B中产生域A和单个参考图像I_B的图像。所提出的算法可以将训练的GaN的任何输出从域A转换为域B.我们的主要优点有两个主要优点方法与当前现有技术相比：首先，我们的解决方案实现了更高的视觉质量，例如通过明显减少过度装箱。其次，我们的解决方案允许更多地控制域间隙的自由度，即图像I_B的哪些方面用于定义域B.从技术上讲，我们通过在预先训练的样式生成器上建立新方法作为GaN和A用于代表域间隙的预先训练的夹模型。我们提出了几种新的常规程序来控制域间隙，以优化预先训练的样式生成器的权重，以输出域B中的图像而不是域A.常规方法防止优化来自单个参考图像的太多属性。我们的结果表明，对现有技术的显着视觉改进以及突出了改进控制的多个应用程序。

We propose semantic region-adaptive normalization (SEAN), a simple but effective building block for Generative Adversarial Networks conditioned on segmentation masks that describe the semantic regions in the desired output image. Using SEAN normalization, we can build a network architecture that can control the style of each semantic region individually, e.g., we can specify one style reference image per region. SEAN is better suited to encode, transfer, and synthesize style than the best previous method in terms of reconstruction quality, variability, and visual quality. We evaluate SEAN on multiple datasets and report better quan-titative metrics (e.g. FID, PSNR) than the current state of the art. SEAN also pushes the frontier of interactive image editing. We can interactively edit images by changing segmentation masks or the style for any given region. We can also interpolate styles from two reference images per region. Code: https://github.com/ZPdesu/SEAN .

Figure 1: (a) and (b): input images; (c): the "two-face" generated by naively copying the left half from (a) and the right half from (b); (d): the "two-face" generated by our Image2StyleGAN++ framework.

We propose an efficient algorithm to embed a given image into the latent space of StyleGAN. This embedding enables semantic image editing operations that can be applied to existing photographs. Taking the StyleGAN trained on the FFHQ dataset as an example, we show results for image morphing, style transfer, and expression transfer. Studying the results of the embedding algorithm provides valuable insights into the structure of the StyleGAN latent space. We propose a set of experiments to test what class of images can be embedded, how they are embedded, what latent space is suitable for embedding, and if the embedding is semantically meaningful.

Effective management of public shared spaces such as car parking space, is one challenging transformational aspect for many cities, especially in the developing World. By leveraging sensing technologies, cloud computing, and Artificial Intelligence, Cities are increasingly being managed smartly. Smart Cities not only bring convenience to City dwellers, but also improve their quality of life as advocated for by United Nations in the 2030 Sustainable Development Goal on Sustainable Cities and Communities. Through integration of Internet of Things and Cloud Computing, this paper presents a successful proof-of-concept implementation of a framework for managing public car parking spaces. Reservation of parking slots is done through a cloud-hosted application, while access to and out of the parking slot is enabled through Radio Frequency Identification (RFID) technology which in real-time, accordingly triggers update of the parking slot availability in the cloud-hosted database. This framework could bring considerable convenience to City dwellers since motorists only have to drive to a parking space when sure of a vacant parking slot, an important stride towards realization of sustainable smart cities and communities.

Driving through pothole infested roads is a life hazard and economically costly. The experience is even worse for motorists using the pothole filled road for the first time. Pothole-filled road networks have been associated with severe traffic jam especially during peak times of the day. Besides not being fuel consumption friendly and being time wasting, traffic jams often lead to increased carbon emissions as well as noise pollution. Moreover, the risk of fatal accidents has also been strongly associated with potholes among other road network factors. Discovering potholes prior to using a particular road is therefore of significant importance. This work presents a successful demonstration of sensor-based pothole mapping agent that captures both the pothole's depth as well as its location coordinates, parameters that are then used to generate a pothole map for the agent's entire journey. The map can thus be shared with all motorists intending to use the same route.