Seongil Im;Jae-Seung Jeong;Junseo Lee;Changhwan Shin;Jeong Ho Cho;Hyunsu Ju
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引用次数: 0
摘要
通过增加给定模型中的参数数量,深度学习最近取得了重大进展。然而,这是以计算资源为代价的,这促使研究人员探索模型压缩技术,以减少参数数量,同时保持甚至提高性能。卷积神经网络(CNN)已被公认为比全连接(FC)网络更高效、更有效。我们在这封信中提出了一种列行卷积神经网络(CRCNN),它将一维卷积应用于图像数据,大大减少了学习参数和操作步骤的数量。CRCNN 利用列和行局部感受野进行数据抽象,在将每个方向的特征连接到 FC 层之前将其串联起来。实验结果表明,与之前的研究相比,CRCNN 在减少参数数量的同时保持了相当的准确性。此外,CRCNN 被用于单类异常检测,证明了它在各种应用中的可行性。
Recent advancements in deep learning have achieved significant progress by increasing the number of parameters in a given model. However, this comes at the cost of computing resources, prompting researchers to explore model compression techniques that reduce the number of parameters while maintaining or even improving performance. Convolutional neural networks (CNN) have been recognized as more efficient and effective than fully connected (FC) networks. We propose a column row convolutional neural network (CRCNN) in this letter that applies 1D convolution to image data, significantly reducing the number of learning parameters and operational steps. The CRCNN uses column and row local receptive fields to perform data abstraction, concatenating each direction's feature before connecting it to an FC layer. Experimental results demonstrate that the CRCNN maintains comparable accuracy while reducing the number of parameters and compared to prior work. Moreover, the CRCNN is employed for one-class anomaly detection, demonstrating its feasibility for various applications.
期刊介绍:
Neural Computation is uniquely positioned at the crossroads between neuroscience and TMCS and welcomes the submission of original papers from all areas of TMCS, including: Advanced experimental design; Analysis of chemical sensor data; Connectomic reconstructions; Analysis of multielectrode and optical recordings; Genetic data for cell identity; Analysis of behavioral data; Multiscale models; Analysis of molecular mechanisms; Neuroinformatics; Analysis of brain imaging data; Neuromorphic engineering; Principles of neural coding, computation, circuit dynamics, and plasticity; Theories of brain function.