We explore the fundamental operations of wavelet transform and convolution from a projective standpoint, then design a highly interpretable, exceptionally lightweight, and fully learnable deep neural network architecture known as Depthwise Separable Axial Asymmetric Wavelet Convolutional Neural Networks (DSAWCN...
文章连接:Wavelet Convolutional Neural Networks 作者文章的动机是为了减少当时网络的计算量。由于深度网络在特征传递过程中,特征本身和梯度信息会随网络传播迅速减少。为了解决这个问题,DenseNet[1]添加了更多的跳跃连接,但类似的解决方式都需要大量的参数,因此也需要大量的计算资源。作者因此想到将CNN与多分辨分析相结合,...
Wavelet neural networkDeep learningImage analysisConvolutional neural network (CNN) is recognized as state of the art of deep learning algorithm, which has a good ability on the image classification and recognition. The problems of CNN are as follows: the precision, accuracy and efficiency of CNN ...
With the integration of convolutional neural networks (CNN) and biomedical engineering, various deep learning models have been proposed to address the challenges of manual feature extraction. Sengur et al. [15], [16] firstly transformed one-dimensional NEMG signals into two-dimensional time–...
本文主要针对Wavelet pooling for convolutional neural networks,ICLR 2018. 简而言之,我认为这篇文章存在一些问题,当然可能是自己理解有误。于是把自己的看法分享出来,希望各位批评指正。 论文地址: Wavelet Pooling for Convolutional Neural Networksopenreview.net/forum?id=rkhlb8lCZ ...
In recent years, there have been attempts to increase the kernel size of Convolutional Neural Nets (CNNs) to mimic the global receptive field of Vision Transformers’ (ViTs) self-attention blocks. That approach, however, quickly hit an upper bound and sa
lpj-github-io/MWCNNv2 Multi-level Wavelet Convolutional Neural Networks Abstract In computer vision, convolutional networks (CNNs) often adopts pooling to enlarge receptive field which has the advantage of low computational complexity. However, pooling can cause information loss and thus is detrimental...
et al. Sensor-based gait parameter extraction with deep convolutional neural networks. IEEE J. Biomed. Health Inform. 21, 85–93 (2016). Article PubMed Google Scholar Caldas, R., Fadel, T., Buarque, F. & Markert, B. Adaptive predictive systems applied to gait analysis: a systematic ...
We explore the fundamental operations of wavelet transform and convolution from a projective standpoint, then design a highly interpretable, exceptionally lightweight, and fully learnable deep neural network architecture known as Depthwise Separable Axial Asymmetric Wavelet Convolutional Neural Networks (DSAWCN...
Convolutional neural network (CNN) is good at learning features from raw data automatically, especially the structural features. Inspired by these, we propose a novel SAR image segmentation method based on convolutional-wavelet neural networks (CWNN) and Markov Random Field (MRF). In this approach,...