n-pair loss是基于一对样本的损失函数,它通过对正负样本进行比较,来评估模型的预测结果。具体来说,对于每个样本对(x, y),其中x是输入特征,y是对应的标签,n-pair loss的计算过程如下: 1. 计算模型预测概率分布P(y|x)与真实标签分布P(y)之间的KL散度; 2. 根据正负样本的标签差异,设定一个阈值δ; 3. 对于...
N-pair-mc loss(multi-class N-pair loss)损失就是文章最后提出的损失。 Triplet loss, (N+1)-tuplet loss, and multi-class N-pair loss with training batch construction. (N+1)元组损失可以定义如下: \mathcal{L}(\{x, x^+, \{x_i\}_{i=1}^{N-1}\}; f) = log(1 + \sum_{i...
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A method includes receiving N pairs of training examples and class labels therefor. Each pair includes a respective anchor example, and a respective non-anchor example capable of being a positive or a negative training example. The method further includes extracting features of the pairs by ...
Batch sampler for the loss function borrowed fromhere. N-pair Loss (NIPS 2016): Sohn, Kihyuk. "Improved Deep Metric Learning with Multi-class N-pair Loss Objective," Advances in Neural Information Processing Systems. 2016. Angular Loss (ICCV 2017): Wang, Jian. "Deep Metric Learning with Ang...
In this paper, we propose to address this problem with a new metric learning objective called multi-class N-pair loss. The proposed objective function firstly generalizes triplet loss by allowing joint comparison among more than one negative examples - more specifically, N -1 negative examples - ...
Improved Deep Metric Learning with Multi-class N-pair Loss Objective - abelard223/Npair_loss_pytorch
Deep Metric Learning Improved Deep Metric Learning with Multi-class N-pair Loss Objective Kihyuk Sohn. NIPS 2016. cited :1 Deep Relative Distance Learning: Tell the Difference Between Similar Vehicles Liu H, Tian Y, Yang Y, et al. CVPR 2016.cited :1 ...
Existing deep hash algorithms are based on paired labels and triple ordering loss, they usually only interact with one negative class, and the convergence speed is too slow. In this paper, we propose a novel deep hashing algorithm called N-pair loss deep hashing (NPLDH), which optimization ...
A method includes receiving N pairs of training examples and class labels therefor. Each pair includes a respective anchor example, and a respective non-anchor example capable of being a positive or a negative training example. The method further includes extracting features of the pairs by ...