与Keras应用程序兼容的EfficientNet噪声学生砝码。 efficientnetb0_notop.h5 efficientnetb1_notop.h5 efficientnetb2_notop.h5 efficientnetb3_notop.h5 efficientnetb4_notop.h5 efficientnetb5_notop.h5 efficientnetb6_notop.h5 efficientnetb7_notop.h5

这是论文“Density Peak Clustering-based Noisy Label Detection for Hyperspectral Image Classification, IEEE Transactions on Geoscience and Remote Sensing, 2018, (Accepted)”的代码，更多细节可以在论文中找到。 如果你使用这个演示，请引用这篇论文。 要运行此演示，您应该先下载 libsvm-3.22。 libsvm-3.22 可在https://www.csie.ntu.edu.tw/~cjlin/libsvm/ 获得

本文主要是研究目的是掌握如何通过双线性变换法设计无限长数字低通滤波器对已加噪声的音乐信号进行滤波。首先通过调用matlab中函数读取一段音乐信号，再对此音乐信号分别加上高斯白噪声、单音频噪声、多音频噪声，之后通过双线性变化方法设计无限长数字脉冲响应低通滤波器，并分别对所加不同噪声的音乐信号进行滤波，并观察滤波前后的时域以及频域波形进行对比。双线性变换法设计滤波器的优点是克服了频谱混叠现象，缺点是数字频率以及模拟频率之间的非线性关系。

噪音峰值 在嘈杂的数据中寻找峰值

嘈杂的学生样本 这是李宏毅教授（李洪义）在NTU-ML-2021弹簧HW3上制作的Noisy Student（ ）的简单实现。 -11移除训练数据上的部分标签。共有11类，有3,080个带标签的训练数据，有6,786个未带标签的训练数据。 数据样本 实验结果 一代 顶级Val Acc 1个 65.7 2个 76.3 3 76.4 4 79.4 5 81.9 程序 请遵循“吵闹的学生”的程序。 首先使用标签数据训练教师模型->使用教师模型在未标签数据上生成伪标签->通过置信度过滤伪标签数据->平衡每个班级的数据数量->组合标签数据和伪标签数据->在新数据集上训练学生模型->将学生模型设为老师模型，然后重复该过程。 请注意，在原始论文中，它们在数据和模型上都执行增强。在数据增强中，使用RandAugment。 在模型扩充中，使用了辍学和随机深度。 但是，此样本未使用随机深度

Tianyi Zhou，Dacheng Tao等人提出的GoDec模型，适用于低秩分解。

In this paper we present a method for fast surface reconstruction from large noisy datasets. Given an unorganized 3D point cloud, our algorithm recreates the underlying surface’s geometrical properties using data resampling and a robust triangulation algorithm in near realtime. For resulting smooth surfaces, the data is resampled with variable densities according to previously estimated surface curvatures. Incremental scans are easily incorporated into an existing surface mesh, by determining the respective overlapping area and reconstructing only the updated part of the surface mesh. The proposed framework is flexible enough to be integrated with additional point label information, where groups of points sharing the same label are clustered together and can be reconstructed separately, thus allowing fast updates via triangular mesh decoupling. To validate our approach, we present results obtained from laser scans acquired in both indoor and outdoor environments.

What is a Noisy-OR Model I am interested with the paper "Noisy-OR Component Analysis and its Application to Link Analysis" published by Tomas Singliar and Milos Hauskrecht on JMLR 7 (2006). A very preliminary prerequisite to understand this paper is to know the "noisy-or" model. However, it seems that noisy-or is an old topic and no much can be found via Google. Fortunately, I got a very brief description from an old paper "Possibility theory and the generalized Noisy OR model". Snapshotting the section on Noisy OR as an image as attached: