mtyka/laplossPublic NotificationsYou must be signed in to change notification settings Fork15 Star43 master BranchesTags Code Latest commit History 3 Commits LICENSE README.md laploss.py as described in this paper:https://arxiv.org/abs/1707.05776"Optimizing the Latent Space of Generative Networks...
Several wave equations for power-law attenuation have a spatial fractional derivative in the loss term. Both one-sided and two-sided spatial fractional derivatives can give causal solutions and a phase velocity dispersion which satisfies the Kramers–Kronig relation. The Chen–Holm and the Treeby–...
function [loss,gradients,lap] = modelLoss(net,X,T) Y = forward(net,X); loss = l1loss(Y,T); X = stripdims(X); Y = stripdims(Y); lap = dllaplacian(Y,X,1); gradients = dlgradient(loss,net.Learnables); end Evaluate the model loss function using the dlfeval function. Get [...
MPSCnnYoloLossDescriptor MPSCnnYoloLossNode MPSCopyAllocator MPSDataLayout MPSDataType MPSGradientNodeHandler MPSGRUDescriptor MPSImage MPSImageAdd MPSImageAllocator_Extensions MPSImageAreaMax MPSImageAreaMin MPSImageArithmetic MPSImageBatch MPSImageBilinearScale MPSImageBox MPSImageConversion MPSImageConvolution ...
Loss function. 为了验证( validate)Charbonnier损失函数的影响,我们用L2损失函数对所建议的网络进行了训练。我们使用更大的学习速率(1e- 4),因为L2的梯度幅值更小。如图2所示,用L2 loss优化的网络(绿线)需要更多的迭代(iterations)才能在SRCNN中获得类似的性能。在图3(d)中,我们展示了用L2损失训练的网络产生了更...
The Laplacian loss measures thedifference of the Laplacians, and correspondingly the difference of the detailstructures, between the content image and a new image. It is flexible andcompatible with the traditional style transfer constraints. By incorporatingthe Laplacian loss, we obtain a new ...
机器学习领域中所谓的降维就是指采用某种映射方法,将原高维空间中的数据点映射到低维度的空间中。降维的本质是学习一个映射函数 f : x->y,其中x是原始数据点的表达,目前最多使用向量表达形式。 y是数据点映射后的低维向量表达,通常y的维度小于x的维度(当然提高维度也是可以的)。f可能是显式的或隐式的、线性...
LapSRN(DeepLaplacianPyramidNetworksforFastandAccurateSuper-Resolution)阅读笔记1、论文地址:https...图片时只采用一次上采样,再有较大的scale时候提高了训练的困难。 3.2、LapSRN 该模型中采用低分辨率图作为输入,并且逐渐的去预测残差图片(加入scale为8,那么就会 ...
The conditional generative adversarial network (CGAN) 是 GAN 的一种拓展。其中,两个网络 G and D 都会收到额外的信息向量ll作为输入。也可以说,训练样本ll的 class 信息,所以 loss function 变成了: 其中,pl(l)pl(l)是类别的先验分布(the prior distribution over classes)。这个模型允许产生器的输出,通过条...
这个地方就是我们的第二层贡献,基于condition 1的性质提出了一个Loss Function使得Loss function的全局最优(其实还有局部最优)能够达到condition 1. 这里请注意(敲黑板),这不是唯一(坑1)的一个loss function能达到condition 1.我们之所以这么设计是因为通过这么设计,我们能和一大类方法Linear Discriminant Analysis,Manif...