To enhance the robustness of the l2-norm elastic full-waveform inversion (FWI), we propose a denoise function that is incorporated into single-frequency gradients. Because field data are noisy and modelled data are noise-free, the denoise function is designed based on the ratio of modelled data...
If a norm is zero, its gradient returns nan: x = Variable(torch.zeros(1), requires_grad=True) x.norm().backward() print x.grad # Variable containing: # nan # [torch.FloatTensor of size 1] Obviously just happening because the gradient div...
But the L2-norm penalty function inherently leads to considerable smoothing of the solution, which reduces the accuracy of distinguishing abnormalities and locating diseased regions. Directly using the L1-norm penalty function, however, may greatly increase computational complexity due to its non-...
The gradient of Lp norm, on the other hand, is very well defined. Which means, any other results can not be justified mathematically. Another use case would be: with torch.enable_grad(): a = torch.tensor([ [3., 0., 4.], [-1., -2., -4.], [-5., 0., 0.], ], requires...
dimensions the authors prove the weak estimate of the first type over uniform tetrahedral partitions of the domain, and give the estimate of the discrete derivative Green function, from which the authors can derive maximum-norm superapproach of the gradient for tetrahedral quadratic finite elements. ...
Gradient flow of the norm squared of a moment map 来自 arXiv.org 喜欢 0 阅读量: 4 作者: E Lerman 摘要: We present a proof due to Duistermaat that the gradient flow of the norm squared of the moment map defines a deformation retract of the appropriate piece of the manifold onto ...
A preprocessing of zero-averaging the gradient Limitation: The gradient can only be weakened to a certain extent, but can not prevent gradientexplosion 2. Zero centered gradient penalty with Linear interpolation Linear interpolation between real and false sample distributions, return gradient norm of||...
1(b), data processing is composed of two steps: distance estimation and phase retrieval. Multi-height holograms are orderly processed by sharpness quantitative metric, and then the distances at each height are Nuclear norm of gradient The key point of auto-focusing imaging lies in how to ...
It can be performed in a number of ways. One option is to simply clip the parameter gradient element-wise before a parameter update. Another option is to clip the norm ||g|| of the gradient g before a parameter update:if ||g||>v then g←gv||g|| where v is a norm threshold....
Deep neural networks (DNNs) are widely used in data analytics, since they deliver state-of-the-art accuracies. Binarized neural networks (BNNs) are recentl... E Nurvitadhi,D Sheffield,J Sim,... - IEEE 被引量: 23发表: 2017年 L1-Norm Batch Normalization for Efficient Training of Deep ...