Apr 14, 2022 · Abstract:Convolutional neural network (CNN) has achieved impressive success in computer vision during the past few decades.
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Jul 20, 2022 · In this paper, we propose a novel computation model, namely CEMNet (Complex Element-wise. Multiplication Network). CEMNet can be viewed as a ...
The proposed method of learning in the frequency domain leverages identical structures of the well- known neural networks, such as ResNet-50, MobileNetV2, and ...
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Apr 14, 2022 · In this paper, we propose a novel neural network model, namely CEMNet, that can be trained in frequency domain. The most important motivation of ...
Mar 7, 2016 · I haven't come across any papers that discuss FFTing the images and filters and then multiplying them instead of convolving them - is any ...
Deep convolutional neural networks (CNNs) are successfully used in a number of applications. However, their storage and computational requirements have.
mowangphy88/TFDMNet: Learning Convolutional Neural Networks in ...
github.com › mowangphy88 › TFDMNet
Learning Convolutional Neural Networks in the Frequency Domain. Our code should work with TensorFlow 2.3 or later. If you hope to use the codes of TFDMNet, ...
Jan 4, 2016 · I presume CNN in the question means “Convolutional Neural Networks” (and not “Cellular Neural Networks”, for instance).
Given the success of DCT in image compression, we are motivated to study the convolutional neural networks compression problem in the DCT frequency domain.
The proposed method of learning in the frequency domain leverages identical structures of the well-known neural networks, such as ResNet-50, MobileNetV2, and ...