Triplet loss 模型显示了 95.88% 的验证准确率,但在识别任务上表现不佳。N-pair-mc 损失模型显着提高了性能。 此外,通过将 N 增加到 320,可以观察到额外的改进,获得 98.33% 的验证、90.17% 的封闭集和 71.76% 的开放集识别精度。 6. N-pair-mc Loss 代码 代码语言:javascript 复制 // N-pair lossimport...
下图是三元组损失(a)、(N+1)元组损失(b)及其改进后的损失(c)的一个对比。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}(\...
contrastive loss 和triplet loss 收敛慢 部分原因是它们仅使用一个负样本而不与每个batch中的其他负类别交互,导致model training的过程中见过的正负样本的情况不充足,特别是对于hard sample pair,本来就不多,可能training的过程中就mining的少很多了,往往需要复杂的hard negative sample mining的方法来辅助。
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...
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
Improved Deep Metric Learning with Multi-class N-pair Loss Objective - ChaofWang/Npair_loss_pytorch
Sentence-Pair+NSP Loss:与原 BERT 相同; Segment-Pair+NSP Loss:输入完整的一对包含多个句子的片段,这些片段可以来自同一个文档,也可以来自不同的文档; Full-Sentences:输入是一系列完整的句子,可以是来自同一个文档也可以是不同的文档; Doc-Sentences:输入是一系列完整的句子,来自同一个文档; ...
Khan Noonien Singh is the most dangerous adversary the Enterprise ever faced. He is brilliant, ruthless, and he will not hesitate to kill every single one of you.Spock Khan Noonien Singh (or simply Khan) was an extremely intelligent and dangerous superhu
“All I’m trying to do is allow people to feel like they’re not alone in their feelings — and there’s so many vast feelings, that’s why I didn’t want to tie it down to just one specific emotion or idea,” he told Billboard about the pair of songs. “I was really trying...