增强卷积神经网络与中频谱集群卷积
IEEE transactions on neural networks and learning systems
|February 8, 2024
概括
本研究介绍了中频谱分组卷积 (MSGC),这是一种用于高效深卷积神经网络的新型模块. MSGC 降低了计算成本,并提高了图像识别任务的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 神经网络架构 神经网络架构
背景情况:
- 深层卷积神经网络 (DCNN) 是计算密集型的.
- 现有的方法,如通道修剪和分组卷积,在平衡效率和准确性方面存在局限性.
- 需要新的模块来优化DCNNs.
研究的目的:
- 为提议和评估一个新的模块,中频谱分组卷积 (MSGC),以提高DCNN的效率.
- 探索道修剪和分组卷积之间的"中频谱",以改进网络设计.
- 为了证明MSGC在降低计算成本的有效性,同时保持或提高准确性.
主要方法:
- 开发了中频谱分组卷积 (MSGC) 模块.
- 将MSGC集成到各种DCNN骨干 (例如ResNet,MobileNetV2) 中.
- 对图像分类 (ImageNet) 和对象检测 (MS COCO) 数据集进行评估的MSGC.
主要成果:
- 对于ResNet-18/50,MSGC将多重积累 (MAC) 降低了50%,Top-1精度提高了1%以上.
- 对于MobileNetV2,MSGC实现了35%的MACs降低,同时提高了Top-1准确度.
- 在MS COCO数据集上的对象检测任务中观察到类似的性能增长.
结论:
- MSGC有效地减少了DCNN中的计算复杂性.
- 拟议的模块可以提高各种计算机视觉任务的预测准确性.
- MSGC为设计高效神经网络提供了一种强大而易于解释的方法.
相关概念视频
Convolution Properties I
151
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:
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:
151
Convolution Properties II
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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...
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
Deconvolution
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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...
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...
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Convolution: Math, Graphics, and Discrete Signals
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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...
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...
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Upsampling
236
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...
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Bandpass Sampling
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In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
180


