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相关概念视频

Downsampling01:20

Downsampling

144
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
144
Upsampling01:22

Upsampling

219
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
219
Deconvolution01:20

Deconvolution

146
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
146
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

239
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
239
Convergence of Fourier Series01:21

Convergence of Fourier Series

139
The Fourier series is a powerful mathematical tool for representing periodic signals as an infinite sum of complex exponentials. In practice, this infinite series is truncated to a finite number of terms, yielding a partial sum. This truncation makes the approximation of the signal feasible but introduces certain challenges, particularly near discontinuities, known as the Gibbs phenomenon.
The Gibbs phenomenon refers to the persistent oscillations and overshoots that occur near discontinuities...
139
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

186
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
186

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Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
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FPWT:过器通过波量变换进行修剪,用于CNNs.

Yajun Liu1, Kefeng Fan2, Wenju Zhou1

  • 1School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.

Neural networks : the official journal of the International Neural Network Society
|August 4, 2024
PubMed
概括
此摘要是机器生成的。

通过波形变换 (WT) 进行过器修剪,可以有效地降低卷积神经网络 (CNN) 的计算成本. 这种方法增强了功能地图分析,用于有效的过器修剪,改善了移动设备上的模型性能.

关键词:
能量加权系数的能量加权系数功能地图的特点地图过器的修剪 过器的修剪波段变换的波段变换是什么

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 卷积神经网络 (CNN) 需要大量的计算资源,这限制了它们在移动设备上的使用.
  • 目前的过器修剪方法主要集中在空间领域,忽视了相互连接和能量分配.
  • 图像频率域转换通过集中能量来提供压缩,这是CNN特征地图所探索的原则.

研究的目的:

  • 为CNN引入一种使用波量变换 (WT) 的新过器修剪方法.
  • 为了利用频域信息,更有效地进行特征地图分析和修剪.
  • 为了减少CNN的计算和参数负载,用于实际的移动应用.

主要方法:

  • 通过波形变换 (FPWT) 开发了过器修剪,将WT与CNN功能地图结合起来.
  • 使用等号相似度和能量加权频率元件计算特征图的重要性.
  • 根据计算的重要性得分进行修剪的过器.

主要成果:

  • FPWT显著降低了FLOP和CNN中的参数.
  • 在ResNet-110 (CIFAR-10) 上,实现了FLOP/参数>60%的减少,精度增加了0.53%.
  • 在ResNet-50 (ImageNet) 上,实现了53.8%的FLOP减少和49.7%的参数减少,而<1%的Top-1精度损失.

结论:

  • 通过分析频域特征,FPWT有效地修剪了CNN的过器.
  • 这种方法可以节省大量的计算成本,对准确度的影响最小.
  • FPWT证明了在资源有限的设备上部署高效的CNN的实用性.