Wavelet convolutional neural networksImage recognition is a very important subject in machine learning. With the increased use of intelligent applications, the image recognition has become more important in various domains. Thus, neural networks and deep learning algorithms has proved a notable success ...
内容提示: Wavelet Convolutional Neural NetworksShin FujiedaThe University of Tokyo, Digital Frontier Inc.sfujieda@graphics.ci.i.u-tokyo.ac.jpKohei TakayamaDigital Frontier Inc.ktakayama@dfx.co.jpToshiya HachisukaThe University of Tokyohachisuka@ci.i.u-tokyo.ac.jpAbstractSpatial and spectral approaches...
Wavelet Pooling小波池化的思考 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 ...
CONVOLUTIONAL neural networksWAVELET transformsCLASSIFICATIONFORECASTINGWavelet transform is a well-known multi-resolution tool to analyze the time series in the time-frequency domain. Wavelet basis is diverse but predefined by manual without taking the data into the consideration. Hence, i...
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...
We present a single image super resolution technique in which we estimate wavelet detail coefficients of a desired high resolution (HR) image using a convolutional neural network (CNN) on the given low resolution (LR) image. Detail coefficients are necessarily sparse for natural images, unlike pixe...
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,...
A multi-channel wide-kernel wavelet convolutional neural network is proposed to address these issues. Firstly, a first layer of multi-channel wide-kernel convolution is designed to fuse different weight information and suppress high-frequency noise. Secondly, a discrete wavelet transform block is ...
This paper uses Convolutional Neural Networks (CNN) to classify handwritten digits in the MNIST database, and scenes in the CIFAR-10 database. Our proposed method preprocesses the data in the wavelet domain to attain greater accuracy and comparable efficiency to the spatial domain processing. By ...
To solve the problem, this letter presents a change detection method based on convolutional-wavelet neural networks (CWNNs). In CWNN, dual-tree complex wavelet transform is introduced into convolutional neural networks for changed and unchanged pixels' classification, and then, the effect of speckle...