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Learning,它是通过机器学习的形式,根据训练数据,自动构造出一种基于特定任务的度量函数。Metric Learning的目标是学习一个变换函数(线性非线性均可)L,将数据点从原始的向量空间映射到一个新的向量空间,在新的向量空间里相似点的距离更近,非相似点的距离更远,使得度量更符合任务的要求,如下图所示。 Deep Metric Learning,就是用深度神经网络来拟合这个变换函数。 +![example](../images/ml_illustration.jpg) + + +## 应用 + Metric Learning技术在生活实际中应用广泛,如我们耳熟能详的人脸识别(Face Recognition)、行人重识别(Person ReID)、图像检索(Image Retrieval)、细粒度分类(Fine-gained classification)等. 随着深度学习在工业实践中越来越广泛的应用,目前大家研究的方向基本都偏向于Deep Metric Learning(DML). + + 一般来说, DML包含三个部分: 特征提取网络来map embedding, 一个采样策略来将一个mini-batch里的样本组合成很多个sub-set, 最后loss function在每个sub-set上计算loss. 如下图所示: + ![image](../images/ml_pipeline.jpg) + + +## 算法 + Metric Learning主要有如下两种学习范式: +### 1. Classification based: + 这是一类基于分类标签的Metric Learning方法。这类方法通过将每个样本分类到正确的类别中,来学习有效的特征表示,学习过程中需要每个样本的显式标签参与Loss计算。常见的算法有[L2-Softmax](https://arxiv.org/abs/1703.09507), [Large-margin Softmax](https://arxiv.org/abs/1612.02295), [Angular Softmax](https://arxiv.org/pdf/1704.08063.pdf), [NormFace](https://arxiv.org/abs/1704.06369), [AM-Softmax](https://arxiv.org/abs/1801.05599), [CosFace](https://arxiv.org/abs/1801.09414), [ArcFace](https://arxiv.org/abs/1801.07698)等。 + 这类方法也被称作是proxy-based, 因为其本质上优化的是样本和一堆proxies之间的相似度。 +### 2. Pairwise based: + 这是一类基于样本对的学习范式。他以样本对作为输入,通过直接学习样本对之间的相似度来得到有效的特征表示,常见的算法包括:[Contrastive loss](http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf), [Triplet loss](https://arxiv.org/abs/1503.03832), [Lifted-Structure loss](https://arxiv.org/abs/1511.06452), [N-pair loss](https://papers.nips.cc/paper/2016/file/6b180037abbebea991d8b1232f8a8ca9-Paper.pdf), [Multi-Similarity loss](https://arxiv.org/pdf/1904.06627.pdf)等 + +2020年发表的[CircleLoss](https://arxiv.org/abs/2002.10857),从一个全新的视角统一了两种学习范式,让研究人员和从业者对Metric Learning问题有了更进一步的思考。 + -- GitLab