因此,Gram矩阵被用于表征图像的风格。在图像修复问题中,很常用的一项损失叫做风格损失(style loss),风格损失正是基于预测结果和真值之间的Gram矩阵的差异构建的。 代码: # 矩阵关系 def gram_matrix(features): N, C, H, W = features.size() feat_reshaped = features.view(N, C, -1) # Use torch.bmm...
格拉姆矩阵Gram matrix 是与协方差矩阵相类似的工具，其具体计算方法和性质在理解了协方差矩阵和相关系数后，便能较好地掌握。Gram矩阵本质是用于计算每个通道的特征映射与其它通道特征映射之间的内积。Gram矩阵中的每个元素，代表了特定通道特征映射与另一个通道特征映射之间的互相关程度。通过将特征图的每个...
Gram Matrix实际上可看做是feature之间的偏心协方差矩阵（即没有减去均值的协方差矩阵），在feature map...
by Charles Sheffield, and relayed to us by Gene Golub, that the QR factorization of an $m imes n$ matrix A via the modified Gram-Schmidt algorithm (MGS) is numerically equivalent to that arising from Householder transformations applied to the matrix A augmented by an n by n zero matrix. ...
Specifically, we perform wavelet threshold denoising on time series to filter normal curve noise, and propose a lossless transformation method based on the Gram matrix, which converts the time series to the time domain image and retains all the information of events. Then, we propose an improved...
f3= f3.view(c, h *w)#Compute gram matrix#图像矩阵点乘矩阵的转置f1 = torch.mm(f1, f1.t())#.t() 转置函数f3 =torch.mm(f3, f3.t())#Compute style loss with target and style imagesstyle_loss += torch.mean((f1 - f3)**2) / (c * h *w)#Compute total loss, backprop and opt...
Efficient Temporal Sequence Comparison and Classification Using Gram Matrix Embeddings on a Riemannian Manifold In this paper we propose a new framework to compare and classify temporal sequences. The proposed approach captures the underlying dynamics of the data whi... X Zhang,W Yin,M Gou,... ...
4) cross-Gramian matrix 交叉格莱姆矩阵 1. Model reduction by minimizing information loss based on cross-Gramian matrix 基于交叉格莱姆矩阵的最小信息损失模型降阶方法 更多例句>> 5) reachability Grammiam 可达性格纳姆矩阵6) Gram matrix criteria 格拉姆(Gram)矩阵判据...
the analysis of error propagation in this algorithm provides new insights in the loss of orthogonality typically observed in the classical Gram-Schmidt method... Y Chahlaoui,K Gallivan,PV Dooren - Society for Industrial and Applied Mathematics 被引量: 36发表: 2003年 Adaptive array beamforming for...
The loss to align the selected basis from the pseudo-inverse of the Gram matrix can be written as: Lcos(ZS, ZT ) = ∥I − M ∥11 (10) with I a vector of ones, of shape p. Minimizing the above term maximizes the cosine similarity between ...