Deep metric learning algorithms have been utilized to learn discriminative
and generalizable models which are effective for classifying unseen classes. In
this paper, a novel noise tolerant deep metric learning algorithm is proposed.
The proposed method, termed as Density Aware Metric Learning, enforces the
model to learn embeddings that are pulled towards the most dense region of the
clusters for each class. It is achieved by iteratively shifting the estimate of
the center towards the dense region of the cluster thereby leading to faster
convergence and higher generalizability. In addition to this, the approach is
robust to noisy samples in the training data, often present as outliers.
Detailed experiments and analysis on two challenging cross-modal face
recognition databases and two popular object recognition databases exhibit the
efficacy of the proposed approach. It has superior convergence, requires lesser
training time, and yields better accuracies than several popular deep metric
learning methods.

本文提出了一种新颖的抗噪声深度度量学习算法，称为密度感知度量学习，其通过将模型迭代地向集群的最密集区域移动来实现更快速的收敛和更高的泛化性，并对两个具有挑战性的跨模态人脸识别数据库和两个流行的物体识别数据库进行了详细的实验和分析，表明该方法具有卓越的收敛性、较短的训练时间以及比其他流行的深度度量学习方法更好的精度。