自从联合学习（FL）被引入具有隐私保护的分散学习技术以来，分布式数据的统计异质性是实现FL应用中实现稳健性能和稳定收敛性的主要障碍。已经研究了模型个性化方法来克服这个问题。但是，现有的方法主要是在完全标记的数据的先决条件下，这在实践中是不现实的，由于需要专业知识。由部分标记的条件引起的主要问题是，标记数据不足的客户可能会遭受不公平的性能增益，因为他们缺乏足够的本地分销见解来自定义全球模型。为了解决这个问题，1）我们提出了一个新型的个性化的半监督学习范式，该范式允许部分标记或未标记的客户寻求与数据相关的客户（助手代理）的标签辅助，从而增强他们对本地数据的认识； 2）基于此范式，我们设计了一个基于不确定性的数据关系度量，以确保选定的帮助者可以提供值得信赖的伪标签，而不是误导当地培训； 3）为了减轻助手搜索引入的网络过载，我们进一步开发了助手选择协议，以实现有效的绩效牺牲的有效沟通。实验表明，与其他具有部分标记数据的相关作品相比，我们提出的方法可以获得卓越的性能和更稳定的收敛性，尤其是在高度异质的环境中。

To obtain lower inference latency and less memory footprint of deep neural networks, model quantization has been widely employed in deep model deployment, by converting the floating points to low-precision integers. However, previous methods (such as quantization aware training and post training quantization) require original data for the fine-tuning or calibration of quantized model, which makes them inapplicable to the cases that original data are not accessed due to privacy or security. This gives birth to the data-free quantization method with synthetic data generation. While current data-free quantization methods still suffer from severe performance degradation when quantizing a model into lower bit, caused by the low inter-class separability of semantic features. To this end, we propose a new and effective data-free quantization method termed ClusterQ, which utilizes the feature distribution alignment for synthetic data generation. To obtain high inter-class separability of semantic features, we cluster and align the feature distribution statistics to imitate the distribution of real data, so that the performance degradation is alleviated. Moreover, we incorporate the diversity enhancement to solve class-wise mode collapse. We also employ the exponential moving average to update the centroid of each cluster for further feature distribution improvement. Extensive experiments based on different deep models (e.g., ResNet-18 and MobileNet-V2) over the ImageNet dataset demonstrate that our proposed ClusterQ model obtains state-of-the-art performance.

基于深度学习的虚拟试用系统最近取得了一些令人鼓舞的进展，但仍然存在需要解决的几个重要挑战，例如尝试所有类型的任意衣服，从一个类别到另一个类别的衣服尝试 - 少数文物的结果。要处理这个问题，我们在本文中首先使用各种类型的衣服，\即顶部，底部和整个衣服收集新的数据集，每个人都有多个类别，具有模式，徽标和其他细节的服装特性丰富的信息。基于此数据集，我们提出了用于全型衣服的任意虚拟试验网络（Avton），这可以通过保存和交易目标衣服和参考人员的特性来综合实际的试验图像。我们的方法包括三个模块：1）四肢预测模块，其用于通过保留参考人物的特性来预测人体部位。这对于处理交叉类别的试验任务（例如长袖\（\ Leftrightarrow \）短袖或长裤（\ Leftrightarrow \）裙子，\等），\等）特别适合，其中暴露的手臂或腿部有皮肤可以合理地预测颜色和细节; 2）改进的几何匹配模块，该模块设计成根据目标人的几何形状的扭曲衣服。通过紧凑的径向功能（Wendland的\（\ PSI \） - 功能），我们改进了基于TPS的翘曲方法; 3）折衷融合模块，即扭转翘曲衣服和参考人员的特点。该模块是基于网络结构的微调对称性来使生成的试验图像看起来更加自然和现实。进行了广泛的模拟，与最先进的虚拟试用方法相比，我们的方法可以实现更好的性能。

High-dimensional linear regression model is the most popular statistical model for high-dimensional data, but it is quite a challenging task to achieve a sparse set of regression coefficients. In this paper, we propose a simple heuristic algorithm to construct sparse high-dimensional linear regression models, which is adapted from the shortest solution-guided decimation algorithm and is referred to as ASSD. This algorithm constructs the support of regression coefficients under the guidance of the least-squares solution of the recursively decimated linear equations, and it applies an early-stopping criterion and a second-stage thresholding procedure to refine this support. Our extensive numerical results demonstrate that ASSD outperforms LASSO, vector approximate message passing, and two other representative greedy algorithms in solution accuracy and robustness. ASSD is especially suitable for linear regression problems with highly correlated measurement matrices encountered in real-world applications.

在深海勘探领域，声纳目前是唯一有效的长距离传感装置。复杂的水下环境，如噪声干扰，低目标强度或背景动态，对声纳成像带来了许多负面影响。其中，非线性强度的问题非常普遍。它也被称为声学传感器成像的各向异性，即当自主水下车辆（AUV）携带声纳从不同角度检测到相同的目标时，图像对之间的强度变化有时非常大，这使得传统匹配算法成为了传统的匹配算法几乎无效。但是，图像匹配是诸如导航，定位和映射等综合任务的基础。因此，获得稳健和准确的匹配结果是非常有价值的。本文提出了一种基于相位信息和深卷积特征的组合匹配方法。它具有两个出色的优势：一个是深度卷积特征可用于衡量声纳图像的本地和全球位置的相似性;另一种是可以在声纳图像的关键目标位置执行本地特征匹配。该方法不需要复杂的手动设计，并以关闭端到端的方式完成非线性强度声纳图像的匹配任务。特征匹配实验在AUV捕获的深海声纳图像上进行，结果表明我们的提议具有卓越的匹配精度和鲁棒性。

最近在计算机愿景领域的研究强烈侧重于深度学习架构来解决图像处理问题。由于传统的计算机视觉方法由于复杂的关系而昂贵，因此，由于传统的计算机视觉方法昂贵，因此在复杂的图像处理方案中经常被认为是昂贵的。但是，共同批判是需要大的注释数据集来确定强大的参数。通过人体专家注释图像是耗时的，繁重，昂贵。因此，需要支持以简化注释，提高用户效率和注释质量。在本文中，我们提出了一种通用的工作流程来帮助注释过程并讨论抽象水平的方法。因此，我们审查了专注于有前途的样本，图像预处理，预标记，标签检查或注释后处理的可能性。此外，我们通过嵌套在混合触摸屏/笔记本电脑设备中的开发灵活和可扩展的软件原型来提出提案的实施。

在深海勘探领域，声纳目前是唯一有效的长距离传感装置。复杂的水下环境，如噪声干扰，低目标强度或背景动态，对声纳成像带来了许多负面影响。其中，非线性强度的问题非常普遍。它也被称为声学成像的各向异性，即，当AUV携带声纳从不同角度检测到相同的目标时，图像对之间的强度差值有时非常大，这使得传统的匹配算法几乎无效。但是，图像匹配是诸如导航，定位和映射等综合任务的基础。因此，获得稳健和准确的匹配结果是非常有价值的。本文提出了一种基于相位信息和深卷积特征的组合匹配方法。它有两个出色的优势：一个是，可以使用深度卷积功能来衡量声纳图像的本地和全球位置的相似性;另一种是可以在声纳图像的关键目标位置执行本地特征匹配。该方法不需要复杂的手动设计，并以关闭端到端的方式完成非线性强度声纳图像的匹配任务。特征匹配实验在AUV捕获的深海声纳图像上进行，结果表明我们的建议具有良好的匹配准确性和鲁棒性。

由于深度学习（主要是深度神经网络）在各种人工智能应用中的主导地位，最近基于深度神经网络（集成深度学习）的合奏学习表明，在改善学习系统的概括方面表现出了重要的表现。但是，由于现代深层神经网络通常具有数百万到数十亿的参数，因此训练多个基础深度学习者和与合奏深层学习者进行测试的时间和空间远大于传统的合奏学习。尽管已经提出了一些快速整体深度学习的算法，以促进某些应用程序中的集合深度学习的部署，但仍需要在特定领域的许多应用程序中取得进一步的进步，在这些领域中，开发时间和计算资源通常受到限制或数据。要处理的是很大的维度。需要解决的紧急问题是如何利用整体深度学习的重要优势，同时减少所需的费用，从而使特定领域的更多应用程序可以从中受益。为了减轻这个问题，必须了解在深度学习时代的合奏学习如何发展。因此，在本文中，我们提出了基本讨论，重点关注已发表的作品，方法，最新进展和传统合奏学习和整体深度学习的不可涉及的数据分析。我们希望本文将有助于实现在深度学习时代，合奏学习未来发展所面临的内在问题和技术挑战。

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 .

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.