6. N-pair-mc Loss 代码 代码语言:javascript 代码运行次数:0 运行 AI代码解释 // N-pair lossimporttorchimporttorch.nn.functionalasFclassNPairMCLoss(torch.nn.Module):def__init__(self,margin=0.1):super(NPairMCLoss,self).__init__()self.margin=margin defforward(self,anchors,positives,negatives)...
方法的名字叫multi-classN-pair loss(N-pair-mc),其构造方式如上图(c)所示。来个说文解字,道一道作者的解决方法。方法名中有个N-pair,就从这入手。假若我们有N个pair: {(x1,x1+),⋯,(xN,xN+},yi≠yj,∀i≠j 每个pair的样本来自不同的类别,在这N个pair的基础上构建N个元组{Si}i=1N,其中: ...
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。 模型见得少...
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 - ...
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Loss functions We implemented loss functions to train the network for image retrieval. 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 ...
finetune:不再mask,直接用图像特征和文本作为输入,得到mean pooling之后的256维向量,pair计算sim后和标签算mse。 2.3 ASL 这是阿里巴巴最新提出的一种用于多标签分类的Loss [3],可以有效解决多标签分类长尾样本的噪声问题。用它来替换baseline中的多标签分类的BCE损失,可以使得收敛更快,最终F1 score也更高。
主要想法:train binary logistic regressions for a true pair (center word and word in its context window) versus several noise pairs (the center word paired with a random word) 目标函数: J(θ)=1TT∑t=1Jt(θ)J(θ)=1T∑t=1TJt(θ) ...
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