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...
向量的内积能体现两个向量间的相似性，因此Gram矩阵反映了特征间的关联情况：哪些特征相互促进，哪些特征相互抑制。Gram矩阵的对角线元素则直接反映了特征在图像中的出现频率。这种特性使Gram矩阵成为表征图像风格的关键工具。在图像修复领域，风格损失(style loss)基于预测结果与真实值之间的Gram矩阵差异构建。...
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...
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...
Finally, we introduce a Triplet network to calculate the similarity between similar events and different classes of events, and optimize the squared loss function of CNN. The proposed T-CNN model can accelerate the convergence rate of gradient descent and improve classification accuracy. Experimental ...
These fusion proteins were secreted into the culture medium, and the QK-3LysM was able to bind to the surface of various LAB GEM particles without a loss of fibrinolytic activity. Furthermore, the binding capacity significantly increased with a higher concentration of QK-3LysM. Compared to the...
Title: Abstract: A method is described that utilizes non-negative matrix factorization to predict susceptibility of a microorganism to an antimicrobial drug. A sparse adjacency matrix is constructed from existing ground truth datasets that include antibiogram data and other data associated with microorgan...
Model reduction by minimizing information loss based oncross-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>> 补充资料:来格司亭,雷诺格拉斯蒂姆,格拉诺赛特 药物名称:格拉诺赛特 英文名:Granocyte 别名:来格司亭,雷诺格拉斯蒂姆,格拉诺赛特 ...
Finally, a new loss function is designed by using hard labels and probability labels of image emotion category to reduce the influence of image emotion subjectivity. Extensive experiments have been conducted on benchmark datasets, including FI (Flickr and Instagram), IAPSsubset, ArtPhoto, and ...
At its core is the Attention Loss, a new loss function devised to penalize self-attention computation between image patches based on the similarity between gram matrices, leading to better convergence and generalization, especially on models pre-trained on mid-size datasets. We deploy the method ...