ComplexNN(Complex Neural Network Modules)是一个对基础神经网络模块进行复值化的GitHub开源项目。ComplexNN 在 PyTorch 框架下提供了复数形式的标准模块,无需任何额外的可训练参数。 该框架中各模块的参数和调用方式与PyTorch框架一致,无需额外的学习成本。 https://github.com/Xinyuan
A high-level toolbox for using complex valued neural networks in PyTorch - GitHub - wavefrontshaping/complexPyTorch: A high-level toolbox for using complex valued neural networks in PyTorch
在实际成像系统中的光学探测器,如CCD和CMOS,只能测量电磁波的大小(亮度或强度)。 相位检索(Phase retrieval, PR),又称相位成像,是指从强度测量中恢复检测过程中丢失的相位信息。 它在科学和工程的许多应用中起着至关重要的作用,包括计算显微术,定量相位成像,x射线晶体学,计算机生成全息术等。 由于光学相位不能由...
Current Python frameworks for deep learning, such as TensorFlow, Keras, PyTorch, and scikit-learn, only solve real-domain problems, representing a considerable part of real-world applications but not all. For instance, complex-valued signals are essential for current and future technologies in ...
Library to help implement a complex-valued neural network (cvnn) using tensorflow as back-end pythonmachine-learningdeep-learningtensorflowmachineneural-networkscomplex-numberscomplex-networkscomplex-neural-networks UpdatedNov 21, 2024 Python Complex-valued neural networks for pytorch and Variational Dropout ...
The increasing interest in filter pruning of convolutional neural networks stems from its inherent ability to effectively compress and accelerate these networks. Currently, filter pruning is mainly divided into two schools: norm-based and relation-based. These methods aim to selectively remove the least...
【文献学习】DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement 简介:本文介绍了一种新的深度复数卷积递归网络(DCCRN),用于处理语音增强问题,特别是针对低模型复杂度的实时处理。 文献地址 Pytorch实现的源码 1 简介和创新点...
Initially, we implemented PhysenNet in PyTorch and assessed its reconstructed results against the provided holograms. Fig. 5(a) and 5(b) display the reconstruction phase for PhysenNet and CVNN, respectively. Both methods effectively restore the contour distribution of the buttons, with detailed ...
PyTorch 1.7.0 was employed to implement the baseline systems of the proposed unsupervised SE framework. The proposed models were trained under an NVIDIA TITAN RTX GPU environment. We used the Adam optimizer [30] with momentum β1=0.45,β2=0.98. The initial learning rate and batch size were...
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