robust_scale
时间: 2023-11-26 19:04:57 浏览: 23
robust_scale是一个函数,它可以在不使用估计器API的情况下进行数据缩放。这个函数可以应用于数据集,以使其具有鲁棒性,即对异常值不敏感。它使用中位数和四分位数来缩放数据,而不是使用平均值和标准差。这个函数可以在sklearn.preprocessing._data中找到,并且它是RobustScaler类的一部分。通过使用robust_scale函数,可以通过移除异常值的影响,对数据进行缩放,以便更好地适应模型的训练和预测过程。
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Multitaper method uses multiple windows (tapers) leading to different subspectra, whose weighted average forms the spectrum estimate and leads to robust MFCCs. For visualization, spectra are shown in dB scale but computations are based on linear values. The tapers are from the SWCE method [9].
多窗口(taper)的使用是多塔普尔方法(Multitaper method)的特点,它会产生不同的子频谱(subspectra),通过对这些子频谱进行加权平均来形成频谱估计,从而得到稳健的MFCCs(Mel频率倒谱系数)。为了可视化,频谱通常以dB刻度显示,但计算过程是基于线性值进行的。这些tapers来自SWCE方法[9]。
翻译成中文:BIFPN stands for "Bi-directional Feature Pyramid Network", which is a neural network architecture used for object detection in computer vision. BIFPN was introduced in a paper titled "BiFPN: Efficient Multi-scale Fusion with Repeated Pyramidal Structures" by Tan et al. in 2019. BIFPN is a type of Feature Pyramid Network (FPN) that aims to improve the performance of object detection models by incorporating multi-scale features. BIFPN achieves this by using a repeated pyramidal structure that combines low-level and high-level features through a bidirectional pathway. In BIFPN, the input features are passed through a series of bi-directional nodes that perform top-down and bottom-up feature fusion, resulting in a set of multi-scale features that are robust to variations in object size and scale. The repeated structure of BIFPN helps to ensure that features at all scales are appropriately integrated, while the bidirectional connections help to propagate information between the high-level and low-level features. BIFPN has been shown to be effective in improving the accuracy of object detection models, while also being computationally efficient. As a result, BIFPN has become a popular choice for many state-of-the-art object detection architectures.
BIFPN的全称是“双向特征金字塔网络”,是一种用于计算机视觉中物体检测的神经网络架构,2019年Tan等人发表的论文《BIFPN:带有重复的金字塔结构的高效多尺度融合》中首次提出。BIFPN是一种特征金字塔网络(FPN),旨在通过结合多尺度特征来提高物体检测模型的性能。BIFPN通过使用一个重复的金字塔结构,通过双向通道将低层特征和高层特征融合在一起,从而实现。在BIFPN中,输入特征通过一系列双向节点进行自上而下和自下而上的特征融合,得到一组对物体大小和尺度变化具有鲁棒性的多尺度特征。BIFPN的重复结构有助于确保所有尺度的特征得到适当的集成,而双向连接有助于在高层特征和低层特征之间传播信息。BIFPN已被证明可以有效提高物体检测模型的准确性,同时具有计算效率。因此,BIFPN已成为许多最先进的物体检测架构的首选。