虚拟试验旨在在店内服装和参考人员图像的情况下产生光真实的拟合结果。现有的方法通常建立多阶段框架来分别处理衣服翘曲和身体混合，或严重依赖基于中间解析器的标签，这些标签可能嘈杂甚至不准确。为了解决上述挑战，我们通过开发一种新型的变形注意流（DAFLOF）提出了一个单阶段的尝试框架，该框架将可变形的注意方案应用于多流量估计。仅将姿势关键点作为指导，分别为参考人员和服装图像估计了自我和跨跨性别的注意力流。通过对多个流场进行采样，通过注意机制同时提取并合并了来自不同语义区域的特征级和像素级信息。它使衣服翘曲和身体合成，同时以端到端的方式导致照片真实的结果。在两个尝试数据集上进行的广泛实验表明，我们提出的方法在定性和定量上都能达到最先进的性能。此外，其他两个图像编辑任务上的其他实验说明了我们用于多视图合成和图像动画方法的多功能性。

低成本单眼的3D对象检测在自主驾驶中起着基本作用，而其精度仍然远非令人满意。在本文中，我们挖掘了3D对象检测任务，并将其重构为对象本地化和外观感知的子任务，这有​​利于整个任务的互惠信息的深度挖掘。我们介绍了一个名为DFR-Net的动态特征反射网络，其中包含两种新的独立模块：（i）首先将任务特征分开的外观定位特征反射模块（ALFR），然后自相互反映互核特征; （ii）通过自学习方式自适应地重建各个子任务的培训过程的动态内部交易模块（DIT）。关于挑战基蒂数据集的广泛实验证明了DFR网的有效性和泛化。我们在基蒂测试集中的所有单眼3D对象探测器中排名第一（直到2021年3月16日）。所提出的方法在许多尖端的3D检测框架中也容易在较忽略的成本下以忽略的成本来播放。该代码将公开可用。

跨域冷启动推荐是推荐系统越来越新兴的问题。现有的作品主要专注于解决跨域用户推荐或冷启动内容推荐。但是，当新域在早期发展时，它具有类似于源域的潜在用户，但互动较少。从源域中学习用户的偏好并将其转移到目标域中是至关重要的，特别是在具有有限用户反馈的新到达内容上。为了弥合这一差距，我们提出了一个自训练的跨域用户偏好学习（夫妻）框架，针对具有各种语义标签的冷启动推荐，例如视频的项目或视频类型。更具体地，我们考虑三个级别的偏好，包括用户历史，用户内容和用户组提供可靠的推荐。利用由域感知顺序模型表示的用户历史，将频率编码器应用于用于用户内容偏好学习的底层标记。然后，建议具有正交节点表示的分层存储器树以进一步概括域域的用户组偏好。整个框架以一种对比的方式更新，以先进先出（FIFO）队列获得更具独特的表示。两个数据集的广泛实验展示了用户和内容冷启动情况的夫妇效率。通过部署在线A / B一周测试，我们表明夫妇的点击率（CTR）优于淘宝应用程序的其他基线。现在该方法在线为跨域冷微视频推荐服务。

由于LIDAR传感器捕获的精确深度信息缺乏准确的深度信息，单眼3D对象检测是一个关键而挑战的自主驾驶任务。在本文中，我们提出了一种立体引导的单目3D对象检测网络，称为SGM3D，其利用立体图像提取的鲁棒3D特征来增强从单眼图像中学到的特征。我们创新地研究了多粒度域适配模块（MG-DA）以利用网络的能力，以便仅基于单手套提示产生立体模拟功能。利用粗均衡特征级以及精细锚级域适配，以引导单眼分支。我们介绍了一个基于IOO匹配的对齐模块（iou-ma），用于立体声和单眼域之间的对象级域适应，以减轻先前阶段中的不匹配。我们对最具挑战性的基蒂和Lyft数据集进行了广泛的实验，并实现了新的最先进的性能。此外，我们的方法可以集成到许多其他单眼的方法中以提高性能而不引入任何额外的计算成本。

条件图像合成旨在根据文本描述，参考图像和图像块的形式创建图像，以保存的，以及它们的组合。在本文中，我们提出了一个新的两级架构M6-UFC，统一了任何数量的多模态控件。在M6-UFC中，各种控制信号和合成图像都均匀地表示为由变压器处理的离散令牌序列。与现有的两级自回归方式不同，如Dall-E和VQGAN，M6-UFC在第二阶段采用非自动发作生成（NAR），以增强合成图像的整体一致性，以支持保留指定的图像块，以及提高合成速度。此外，我们设计了一种逐步算法，其迭代地改善了非自动产生的图像，其中包括用于评估符合控制的符合和评估合成图像的保真度的两个估计器的帮助。在新收集的大型服装数据集M2C时装和面部数据集多模态Celeba-HQ上进行了广泛的实验验证了M6-UFC可以合成符合灵活的多模态控制的高保真图像。

With the development of experimental quantum technology, quantum control has attracted increasing attention due to the realization of controllable artificial quantum systems. However, because quantum-mechanical systems are often too difficult to analytically deal with, heuristic strategies and numerical algorithms which search for proper control protocols are adopted, and, deep learning, especially deep reinforcement learning (RL), is a promising generic candidate solution for the control problems. Although there have been a few successful applications of deep RL to quantum control problems, most of the existing RL algorithms suffer from instabilities and unsatisfactory reproducibility, and require a large amount of fine-tuning and a large computational budget, both of which limit their applicability. To resolve the issue of instabilities, in this dissertation, we investigate the non-convergence issue of Q-learning. Then, we investigate the weakness of existing convergent approaches that have been proposed, and we develop a new convergent Q-learning algorithm, which we call the convergent deep Q network (C-DQN) algorithm, as an alternative to the conventional deep Q network (DQN) algorithm. We prove the convergence of C-DQN and apply it to the Atari 2600 benchmark. We show that when DQN fail, C-DQN still learns successfully. Then, we apply the algorithm to the measurement-feedback cooling problems of a quantum quartic oscillator and a trapped quantum rigid body. We establish the physical models and analyse their properties, and we show that although both C-DQN and DQN can learn to cool the systems, C-DQN tends to behave more stably, and when DQN suffers from instabilities, C-DQN can achieve a better performance. As the performance of DQN can have a large variance and lack consistency, C-DQN can be a better choice for researches on complicated control problems.

In this thesis, we consider two simple but typical control problems and apply deep reinforcement learning to them, i.e., to cool and control a particle which is subject to continuous position measurement in a one-dimensional quadratic potential or in a quartic potential. We compare the performance of reinforcement learning control and conventional control strategies on the two problems, and show that the reinforcement learning achieves a performance comparable to the optimal control for the quadratic case, and outperforms conventional control strategies for the quartic case for which the optimal control strategy is unknown. To our knowledge, this is the first time deep reinforcement learning is applied to quantum control problems in continuous real space. Our research demonstrates that deep reinforcement learning can be used to control a stochastic quantum system in real space effectively as a measurement-feedback closed-loop controller, and our research also shows the ability of AI to discover new control strategies and properties of the quantum systems that are not well understood, and we can gain insights into these problems by learning from the AI, which opens up a new regime for scientific research.

Motivated by the increasing application of low-resolution LiDAR recently, we target the problem of low-resolution LiDAR-camera calibration in this work. The main challenges are two-fold: sparsity and noise in point clouds. To address the problem, we propose to apply depth interpolation to increase the point density and supervised contrastive learning to learn noise-resistant features. The experiments on RELLIS-3D demonstrate that our approach achieves an average mean absolute rotation/translation errors of 0.15cm/0.33\textdegree on 32-channel LiDAR point cloud data, which significantly outperforms all reference methods.

Due to the complicated nanoscale structures of current integrated circuits(IC) builds and low error tolerance of IC image segmentation tasks, most existing automated IC image segmentation approaches require human experts for visual inspection to ensure correctness, which is one of the major bottlenecks in large-scale industrial applications. In this paper, we present the first data-driven automatic error detection approach targeting two types of IC segmentation errors: wire errors and via errors. On an IC image dataset collected from real industry, we demonstrate that, by adapting existing CNN-based approaches of image classification and image translation with additional pre-processing and post-processing techniques, we are able to achieve recall/precision of 0.92/0.93 in wire error detection and 0.96/0.90 in via error detection, respectively.

我们考虑偏微分方程（PDE）的逆问题，以便依赖关系结构的参数可以随着时间的流逝而表现出随机变更点。例如，当物理系统处于恶意攻击下（例如，黑客对电网和互联网网络的攻击）或遭受极端外部条件（例如，影响电网的天气条件或大型市场移动）影响衍生性的估值时，可能会发生这种情况。合同）。为此，我们采用了物理知情的神经网络（PINNS） - 可以合并PDE系统所描述的任何物理定律的普遍近似值。这种先验的知识在神经网络的训练中起作用，是限制可接受解决方案空间并增加功能近似的正确性的正规化。我们表明，当真实的数据生成过程在PDE动力学中表现出更改点时，这种正则化会导致完整的错过校准和模型的故障。因此，我们建议使用总差异惩罚扩展PINN，该惩罚适合PDE动力学中的（多个）变更点。这些更改点可以随着时间的推移在随机位置发生，并且它们与解决方案一起估计。我们提出了一种附加的完善算法，该算法将更改点检测到可用于计算强化PINNS方法的动态编程方法的减少的动态编程方法结合在一起，我们证明了使用不同方程式的示例与参数变化的不同方程式的示例，证明了所提出的模型的好处。如果数据中没有更改点，则提出的模型将减少为原始PINNS模型。在存在变更点的情况下，与原始PINNS模型相比，它会导致参数估计，更好的模型拟合和较低的训练误差的改进。