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相关概念视频

Upsampling01:22

Upsampling

242
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...
242
Downsampling01:20

Downsampling

167
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...
167
Reducing Line Loss01:18

Reducing Line Loss

156
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
156
Deconvolution01:20

Deconvolution

168
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...
168
Active Filters01:25

Active Filters

837
Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
837
Survival Tree01:19

Survival Tree

89
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
89

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相关实验视频

Updated: Jul 13, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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Published on: December 15, 2023

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操纵相同的过器冗余性,以在深度和复杂的CNN上有效地修剪.

Tianxiang Hao, Xiaohan Ding, Jungong Han

    IEEE transactions on neural networks and learning systems
    |October 12, 2023
    PubMed
    概括

    中心性SGD (C-SGD) 通过创建相同的过器来组织卷积神经网络 (CNN) 的冗余. 这有助于高效的网络修剪,不损失精度或微调,提高CNN的性能.

    科学领域:

    • 人工智能的人工智能
    • 计算机视觉 计算机视觉
    • 机器学习 机器学习

    背景情况:

    • 卷积神经网络 (CNN) 具有固有的冗余性,允许过器/通道去除.
    • 目前的CNN培训目标忽视了冗余性,导致随机分布和在修剪时潜在的准确性下降.
    • 现有的方法通常需要在修剪后进行广泛的微调,以恢复性能.

    研究的目的:

    • 提出一种新的培训方法,利用冗余来进行有效的网络修剪.
    • 开发一种技术,以促进删除冗余的过器/通道,而不会影响网络性能.
    • 通过有组织的冗余来提高CNN的效率和准确性.

    主要方法:

    • 介绍了一种新型的优化算法:偏心的随机梯度下降 (C-SGD).
    • 在训练期间,C-SGD故意创建相同的过器,建立理想的冗余模式.
    • 在所有层面同时应用C-SGD,包括非常深的CNN.

    主要成果:

    • 与现有方法相比,C-SGD在CIFAR和ImageNet数据集上表现出优异的性能.
    • 通过C-SGD实现的有组织冗余导致更好的网络效率和准确性.
    • 该方法可以在不需要培训后微调的情况下进行修剪.

    更多相关视频

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    Deep Neural Networks for Image-Based Dietary Assessment
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    结论:

    • C-SGD有效地组织了CNN中的冗余,简化了网络修剪.
    • 拟议的方法为CNN优化和压缩提供了一种高效和准确的方法.
    • C-SGD为开发更简单,更高性能的深度学习模型提供了一个有希望的方向.