def forward(self, output1, output2, label): euclidean_distance = F.pairwise_distance(output1, output2) loss_contrastive = torch.mean((1 - label) * torch.pow(euclidean_distance, 2) + (label) * torch.pow(torch.clamp(self.margin - euclidean_distance, min=0.0), 2)) return loss_contrast...
If Bruno's solution is what you want, and you have Statistics Toolbox, you can use pdist to calculate all the pairwise distances, then max to find the biggest (and indexing to find the locations). 댓글 수: 0 댓글을 달려면 로그인하십시오.이...
Max-min distance analysis (MMDA) address- es this problem by maximizing the minimum pairwise dis- tance in the latent subspace, but it is developed under the homoscedastic assumption. This paper proposes Het- eroscedastic MMDA (HMMDA) methods that explore the discriminative information in the ...
1importpickle2importnumpy as np3importpandas as pd4importnetworkx as nx5importgeopandas as gpd6importscipy.sparse as sp7importmatplotlib.pyplot as plt8fromscipy.spatialimportcKDTree9importxml.etree.cElementTree as et10fromjoblibimportParallel, delayed11fromshapely.geometryimportPoint, MultiLineString, Po...
Max–min distance analysis (MMDA) performs dimensionality reduction by maximizing the minimum pairwise distance between classes in the latent subspace under the homoscedastic assumption, which can address the class separation problem caused by the Fisher criterion but is incapable of tackling heteroscedasti...
Metric learning 的方法会显式地优化“类内样本差异小”和“类间样本差异大”的目标,也被广泛应用于 face recognition,例如,[DeepID2]同时使用了 softmax loss 和 contrastive loss (pairwise loss), 著名的 [FaceNet] 仅仅使用 triplet loss 就能得到表现良好的 feature。然而,简单 Metric Learning 是不够的。
The sum-dispersion function f(S)= 鈭慱(x,y鈭圫) d(x, y), which is the sum of the pairwise distances in S, is in this context a prominent diversification measure. The corresponding diversity maximization is the max-sum or sum-sum diversification. Many recent results deal with the ...
Calculates pairwise distances among image features to determine the bandwidth for the Gaussian kernel. Initializes the affinity matrix based on the softmax of scaled logits. Sets initial values for inlierness scores (y) uniformly. Iterative optimization: Alternates between updating inlierness scores ...
PairwiseDistance.lua Parallel.lua ParallelCriterion.lua ParallelTable.lua PartialLinear.lua PixelShuffle.lua Power.lua PrintSize.lua Profile.lua README.md RReLU.lua ReLU.lua ReLU6.lua Replicate.lua Reshape.lua Select.lua SelectTable.lua Sequential.lua Sigmoid.lua SmoothL1Criterion.lua SoftMarginCrite...
Metric learning 的方法会显式地优化“类内样本差异小”和“类间样本差异大”的目标,也被广泛应用于 face recognition,例如,[DeepID2]同时使用了 softmax loss 和 contrastive loss (pairwise loss), 著名的 [FaceNet] 仅仅使用 triplet loss 就能得到表现良好的 feature。然而,简单 Metric Learning 是不够的。