Existing neural rendering methods for creating human avatars typically either require dense input signals such as video or multi-view images, or leverage a learned prior from large-scale specific 3D human datasets such that reconstruction can be performed with sparse-view inputs. Most of these methods fail to achieve realistic reconstruction when only a single image is available. To enable the data-efficient creation of realistic animatable 3D humans, we propose ELICIT, a novel method for learning human-specific neural radiance fields from a single image. Inspired by the fact that humans can easily reconstruct the body geometry and infer the full-body clothing from a single image, we leverage two priors in ELICIT: 3D geometry prior and visual semantic prior. Specifically, ELICIT introduces the 3D body shape geometry prior from a skinned vertex-based template model (i.e., SMPL) and implements the visual clothing semantic prior with the CLIP-based pre-trained models. Both priors are used to jointly guide the optimization for creating plausible content in the invisible areas. In order to further improve visual details, we propose a segmentation-based sampling strategy that locally refines different parts of the avatar. Comprehensive evaluations on multiple popular benchmarks, including ZJU-MoCAP, Human3.6M, and DeepFashion, show that ELICIT has outperformed current state-of-the-art avatar creation methods when only a single image is available. Code will be public for reseach purpose at https://elicit3d.github.io .

本文考虑了从野外单视图像中无监督的3D对象重建的问题。由于歧义性和内在的不良性，这个问题本质上难以解决，因此需要强大的正则化以实现不同潜在因素的分离。与现有的作品将明确的正规化引入目标功能不同，我们研究了一个不同的空间进行隐式正则化 - 潜在空间的结构。具体而言，我们限制了潜在空间的结构，以捕获潜在因素的拓扑因果排序（即代表因果关系作为定向无环形图）。我们首先表明，不同的因果顺序对于3D重建至关重要，然后探索几种方法以找到与任务有关的因果因素排序。我们的实验表明，潜在空间结构确实是隐式正规化，并引入了有益于重建的电感偏见。

如今，对大规模数据的深入学习是主导的。空前的数据规模可以说是深度学习成功的最重要的驱动力之一。但是，仍然存在收集数据或标签可能非常昂贵的场景，例如医学成像和机器人技术。为了填补这一空白，本文考虑了使用少量代表性数据从头开始研究的问题。首先，我们通过在球形歧管的同构管上积极学习来表征这个问题。这自然会产生可行的假设类别。使用同源拓扑特性，我们确定了一个重要的联系 - 发现管歧管等同于最大程度地减少物理几何形状中的超球能（MHE）。受此连接的启发，我们提出了一种基于MHE的主动学习（MHEAL）算法，并为MHEAL提供了全面的理论保证，涵盖了收敛和概括分析。最后，我们证明了MHEAL在数据效率学习的广泛应用中的经验表现，包括深度聚类，分布匹配，版本空间采样和深度积极学习。

机器学习模型的基本挑战是由于杂散的相关性部分地推广到分销（OOD）数据。为了解决这一挑战，我们首先将“ood泛化问题”正式形式化为受限制的优化，称为解剖学限制域泛化（DDG）。我们以有限维参数化和经验逼近的方式将该非普通约束优化放宽到贸易形式。然后，提供了对上述变换偏离原始问题的程度的理论分析。基于转型，我们提出了一种用于联合表示解剖和域泛化的原始双向算法。与基于领域对抗性培训和域标签的传统方法形成鲜明对比，DDG共同学习解剖学的语义和变化编码器，使灵活的操纵和增强训练数据。 DDG旨在学习语义概念的内在表示，这些概念不变于滋扰因素，并遍布不同的域。对流行基准的综合实验表明，DDG可以实现竞争性的ood性能，并在数据中揭示可解释的突出结构。

人工智能（AI）为简化Covid-19诊断提供了有前景的替代。然而，涉及周围的安全和可信度的担忧阻碍了大规模代表性的医学数据，对临床实践中训练广泛的模型造成了相当大的挑战。为了解决这个问题，我们启动了统一的CT-Covid AI诊断计划（UCADI），其中AI模型可以在没有数据共享的联合学习框架（FL）下在每个主机机构下分发和独立地在没有数据共享的情况下在每个主机机构上执行。在这里，我们认为我们的FL模型通过大的产量（中国测试敏感性/特异性：0.973 / 0.951，英国：0.730 / 0.942），与专业放射科医师的面板实现可比性表现。我们进一步评估了持有的模型（从另外两家医院收集，留出FL）和异构（用造影材料获取）数据，提供了模型所做的决策的视觉解释，并分析了模型之间的权衡联邦培训过程中的性能和沟通成本。我们的研究基于来自位于中国和英国的23家医院的3,336名患者的9,573次胸部计算断层扫描扫描（CTS）。统称，我们的工作提出了利用联邦学习的潜在保留了数字健康的前景。

在本文中，我们考虑了迭代机教学问题，教师根据当前迭代学习者顺序提供示例。与必须扫描整个池并在每次迭代中选择教学示例的先前方法相比，我们提出了一个标签综合教学框架，其中教师随机选择输入教学示例（例如，图像），然后合成合适的输出（例如，，标签）为他们。我们表明，此框架可以避免昂贵的示例选择，同时仍然可以获得指数的可行性。我们在本框架中提出了多种新颖的教学算法。最后，我们经验证明了我们框架的价值。

由于过度参数化性质，神经网络是一个强大的非线性函数近似的工具。为了在看不见的数据上实现良好的概括，适当的归纳偏差对于神经网络来说是非常重要的。最直接的方式之一是将神经网络与一些额外的目标进行规范化。L2正则化用作神经网络的标准正则化。尽管其受欢迎程度，但它基本上规范了个体神经元的一个维度，这不足以控制高度过度参数化神经网络的能力。由此激励，提出了高度球形的均匀性作为影响神经元之间相互作用的新型关系规则。我们考虑了几种几何鲜明的方式来实现超球均匀性。高度球形均匀性的有效性是由理论洞察力和经验评估的合理性。

In this paper, we propose a conceptually simple and geometrically interpretable objective function, i.e. additive margin Softmax (AM-Softmax), for deep face verification. In general, the face verification task can be viewed as a metric learning problem, so learning large-margin face features whose intra-class variation is small and inter-class difference is large is of great importance in order to achieve good performance. Recently, Large-margin Softmax [10] and Angular Softmax [9] have been proposed to incorporate the angular margin in a multiplicative manner. In this work, we introduce a novel additive angular margin for the Softmax loss, which is intuitively appealing and more interpretable than the existing works. We also emphasize and discuss the importance of feature normalization in the paper. Most importantly, our experiments on LFW and MegaFace show that our additive margin softmax loss consistently performs better than the current state-of-the-art methods using the same network architecture and training dataset. Our code has also been made available 1 .

This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space. However, few existing algorithms can effectively achieve this criterion. To this end, we propose the angular softmax (A-Softmax) loss that enables convolutional neural networks (CNNs) to learn angularly discriminative features. Geometrically, A-Softmax loss can be viewed as imposing discriminative constraints on a hypersphere manifold, which intrinsically matches the prior that faces also lie on a manifold. Moreover, the size of angular margin can be quantitatively adjusted by a parameter m. We further derive specific m to approximate the ideal feature criterion. Extensive analysis and experiments on Labeled Face in the Wild (LFW), Youtube Faces (YTF) and MegaFace Challenge show the superiority of A-Softmax loss in FR tasks. The code has also been made publicly available 1 .

Cross-entropy loss together with softmax is arguably one of the most common used supervision components in convolutional neural networks (CNNs). Despite its simplicity, popularity and excellent performance, the component does not explicitly encourage discriminative learning of features. In this paper, we propose a generalized large-margin softmax (L-Softmax) loss which explicitly encourages intra-class compactness and inter-class separability between learned features. Moreover, L-Softmax not only can adjust the desired margin but also can avoid overfitting. We also show that the L-Softmax loss can be optimized by typical stochastic gradient descent. Extensive experiments on four benchmark datasets demonstrate that the deeply-learned features with L-softmax loss become more discriminative, hence significantly boosting the performance on a variety of visual classification and verification tasks.