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相关概念视频

Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

393
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...
393
Convolution Properties I01:20

Convolution Properties I

232
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:
232
Convolution Properties II01:17

Convolution Properties II

278
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...
278
Reducing Line Loss01:18

Reducing Line Loss

193
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...
193
Downsampling01:20

Downsampling

250
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...
250
Vector Operations01:20

Vector Operations

1.5K
Vectors are physical quantities that have both magnitude and direction. The vector operations include addition, subtraction, and scalar multiplication.
A vector multiplied by a scalar value is called scalar multiplication. The result obtained is a new vector with a different magnitude. If the scalar is positive, the direction of the vector remains the same, but if it is negative, the direction of the vector is reversed. For example, the product of the mass and velocity yields the momentum.
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相关实验视频

Updated: Sep 9, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

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减少储存直接张量环分解用于卷积神经网络压缩

Mateusz Gabor1, Rafał Zdunek1

  • 1Faculty of Electronics, Photonics, and Microsystems, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, Wroclaw, 50-370, Poland.

Neural networks : the official journal of the International Neural Network Society
|September 4, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的低级方法,用于使用减少存储直接张量环分解 (RSDTR) 压缩卷积神经网络 (CNN). 在保持高图像分类精度的同时,RSDTR显著减少了模型大小和计算.

关键词:
卷积神经网络减少储存低级压缩张量环的分解

更多相关视频

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

491

相关实验视频

Last Updated: Sep 9, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.6K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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科学领域:

  • 计算机视觉
  • 机器学习
  • 深度学习的优化

背景情况:

  • 卷积神经网络 (CNN) 对于像图像分类这样的计算机视觉任务至关重要.
  • 在存储和计算方面,模型压缩对于提高CNN效率至关重要.
  • 低级近似方法为CNN压缩提供了一个有希望的途径,通过分解大型内核.

研究的目的:

  • 提出一种新的低级压缩方法.
  • 通过减少储存直接张量环分解 (RSDTR) 来实现高效的内核近似.
  • 评估RSDTR在实现高压缩率和保持精度方面的有效性.

主要方法:

  • 开发了一种基于RSDTR的新型低级CNN压缩技术.
  • 实现了RSDTR以近似卷积内核,减少参数和FLOPS复杂性.
  • 在CIFAR-10和ImageNet数据集上进行实验以评估性能.

主要成果:

  • 拟议的RSDTR方法实现了显著的参数和FLOPS压缩率.
  • 与现有方法相比,RSDTR显示出更高的循环模式转换灵活性.
  • 使用RSDTR的压缩网络保持了具有竞争力的分类准确性.

结论:

  • RSDTR是一种有效的压缩CNN的方法.
  • 这种方法在压缩效率和分类性能之间提供了有利的权衡.
  • 它的性能优于其他最先进的CNN压缩技术.