This paper studies the distribution estimation of contaminated data by the MoM-GAN method, which combines generative adversarial net (GAN) and median-of-mean (MoM) estimation. We use a deep neural network (DNN) with a ReLU activation function to model the generator and discriminator of the GAN. Theoretically, we derive a non-asymptotic error bound for the DNN-based MoM-GAN estimator measured by integral probability metrics with the $b$-smoothness H\"{o}lder class. The error bound decreases essentially as $n^{-b/p}\vee n^{-1/2}$, where $n$ and $p$ are the sample size and the dimension of input data. We give an algorithm for the MoM-GAN method and implement it through two real applications. The numerical results show that the MoM-GAN outperforms other competitive methods when dealing with contaminated data.

在统计和机器学习中具有重尾数据的模型开发强大的估计估计兴趣兴趣。本文提出了一个用于大家庭统计回归的日志截断的M估计，并在数据具有$ \ varepsilon \中的数据（0，1] $。随着相关风险函数的额外假设，我们获得了估计的$ \ ell_2 $ -Error绑定。我们的定理应用于建立具体回归的强大M估计。除了凸面回归等分位数回归之外广义线性模型，许多非凸回归也可以符合我们的定理，我们专注于强大的深度神经网络回归，这可以通过随机梯度下降算法解决。模拟和实际数据分析证明了日志截断估计的优越性超过标准估计。

在机器学习和高维统计领域的有限样本理论中，恒定指定的浓度不平等至关重要。我们获得了独立亚网络随机变量总和的更清晰和常数的浓度不平等，这导致了两个尾巴的混合物：尺寸的小偏差和较大偏差的小偏差。这些界限是新的，并通过更清晰的常数改善了现有的界限。另外，如果应保留斜体，则新的子韦布尔参数。请检查整个文本。还提出了提出的，它可以为随机变量（向量）恢复紧密浓度不平等。对于统计应用，我们给出了$ \ ell_2 $ - 估计系数在负二项式回归中的估计系数时，当重尾协变量是稀疏结构分布的亚weibull时，这是负二项式回归的新结果。在应用随机矩阵时，我们得出了Bai-Yin定理的非反应版本，用于具有指数尾巴边界的亚weibull条目。最后，通过为没有第二瞬间条件的对数截断的Z-测验器演示一个子静电区域，我们讨论并定义了独立观测值的sub-weibull类型稳健估计器$ \ {x_i \} _ {i = 1 }^{n} $没有指数矩条件。

The processing and recognition of geoscience images have wide applications. Most of existing researches focus on understanding the high-quality geoscience images by assuming that all the images are clear. However, in many real-world cases, the geoscience images might contain occlusions during the image acquisition. This problem actually implies the image inpainting problem in computer vision and multimedia. To the best of our knowledge, all the existing image inpainting algorithms learn to repair the occluded regions for a better visualization quality, they are excellent for natural images but not good enough for geoscience images by ignoring the geoscience related tasks. This paper aims to repair the occluded regions for a better geoscience task performance with the advanced visualization quality simultaneously, without changing the current deployed deep learning based geoscience models. Because of the complex context of geoscience images, we propose a coarse-to-fine encoder-decoder network with coarse-to-fine adversarial context discriminators to reconstruct the occluded image regions. Due to the limited data of geoscience images, we use a MaskMix based data augmentation method to exploit more information from limited geoscience image data. The experimental results on three public geoscience datasets for remote sensing scene recognition, cross-view geolocation and semantic segmentation tasks respectively show the effectiveness and accuracy of the proposed method.

在本文中，我们考虑了使用$ \ ell_1 $ regularized logistic回归的方法来估算与高维iSing模型相关的图形的元学习问题，用于每个节点的邻域选择。我们的目标是在学习新任务中使用从辅助任务中学到的信息来降低其足够的样本复杂性。为此，我们提出了一种新颖的生成模型以及不当的估计方法。在我们的设置中，所有任务均为\ emph {相似}在其\ emph {Random}模型参数和支持中。通过将所有样品从辅助任务汇总到\ emph {不正确}估计一个参数向量，我们可以恢复假定的尺寸很小的真实支持联合，具有很高的概率，具有足够的样品复杂性为$ \ omega（1） $每任务，对于$ k = \ omega（d^3 \ log P）$具有$ p $节点和最大邻域大小$ d $的ISING型号的任务。然后，在对新任务的支持仅限于估计的支持联盟的支持下，我们证明，可以通过降低$ \ omega（d^3 \ log d）$的足够样品复杂性来获得新任务的一致邻居选择。

联合优化（FedOpt），在大量分布式客户端协作培训学习模型的目标是对联邦学习的重要性。 Fedopt的主要问题可归因于模型分歧和通信效率，这显着影响了性能。在本文中，我们提出了一种新方法，即Losac，更有效地从异构分布式数据中学习。它的关键算法洞察力是在{每个}常规本地模型更新之后本地更新全局全梯度的估计。因此，Losac可以使客户的信息以更紧凑的方式刷新。特别是，我们研究了Losac的收敛结果。此外，Losac的奖金是能够从最近的技术泄漏梯度（DLG）中捍卫信息泄漏。最后，实验已经验证了与最先进的FedOpt算法比较Losac的优越性。具体而言，Losac平均超过100美元的价格提高了通信效率，减轻了模型分歧问题，并配备了对抗DLG的防御能力。

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.

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.