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相关概念视频

Convolution Properties II01:17

Convolution Properties II

199
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
199
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

254
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...
254
Convolution Properties I01:20

Convolution Properties I

149
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
149
Deconvolution01:20

Deconvolution

159
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...
159
Survival Tree01:19

Survival Tree

85
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...
85

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相关实验视频

Updated: Jul 1, 2025

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

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复杂的混合加权修剪方法用于加速卷积神经网络.

Xu Geng1, Jinxiong Gao1, Yonghui Zhang2

  • 1School of Information and Communication Engineering, Hainan University, Haikou, 570228, China.

Scientific reports
|March 6, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的混合加权修剪方法,用于卷积神经网络,显著减少计算,同时保持高性能. 这种方法有效地削减了过器,并考虑了批量规范化层,以获得更好的网络压缩.

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Deep Neural Networks for Image-Based Dietary Assessment
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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) 是计算密集型的,需要高效的压缩和加速技术.
  • 当前的过器修剪方法,如基于规范和基于关系的方法,经常忽视过器多样性和批量规范化层的影响,可能会降低性能.

研究的目的:

  • 为了解决现有的过器修剪方法的局限性.
  • 引入一种新的复杂混合加权修剪方法,以提高过器修剪的有效性和稳定性.

主要方法:

  • 基于规范和基于相似性的削减缺点的实证分析.
  • 开发一种复杂的混合加权修剪方法,评估波器相关性,规范和批量规范化参数.
  • 使用ImageNet和CIFAR-10数据集对ResNet架构进行全面的修剪实验.

主要成果:

  • 拟议的混合加权修剪方法有效地识别和删除多余的过器,而不会显著降低性能.
  • 在ImageNet上为ResNet-50实现了53.5%的浮点操作减少,性能损失仅为0.6%.
  • 在不同的ResNet深度和数据集中表现出显著的有效性.

结论:

  • 复杂的混合加权修剪方法为压缩CNN提供了强大而有效的解决方案.
  • 这种方法成功地平衡了网络压缩与性能维护,优于现有的方法.