论文标题
剥夺多相似性公式：一种自定进度的课程驱动方法，用于强大的度量学习
Denoising Multi-Similarity Formulation: A Self-paced Curriculum-Driven Approach for Robust Metric Learning
论文作者
论文摘要
深度度量学习（DML）是一组技术，旨在通过神经网络来衡量对象之间的相似性。尽管近年来DML方法的数量迅速增加，但大多数以前的研究无法有效处理嘈杂的数据，这些数据通常存在于实际应用中，并且通常导致严重的性能恶化。 To overcome this limitation, in this paper, we build a connection between noisy samples and hard samples in the framework of self-paced learning, and propose a \underline{B}alanced \underline{S}elf-\underline{P}aced \underline{M}etric \underline{L}earning (BSPML) algorithm with a denoising multi-similarity配方，嘈杂的样品被视为极其硬样品，并通过样品加权自适应地排除了模型训练。特别是，由于成对关系和新的平衡正规化项，子问题\ emph {w.r.t。}样本权重是一个非convex二次函数。为了有效地解决此非convex二次问题，我们提出了一种双重的随机投影坐标梯度算法。重要的是，从理论上讲，我们不仅证明了与双随机投影坐标梯度算法的收敛性，而且还证明了我们的BSPML算法的收敛性。几个标准数据集的实验结果表明，与最新的鲁棒DML方法相比，我们的BSPML算法具有更好的概括能力和鲁棒性。
Deep Metric Learning (DML) is a group of techniques that aim to measure the similarity between objects through the neural network. Although the number of DML methods has rapidly increased in recent years, most previous studies cannot effectively handle noisy data, which commonly exists in practical applications and often leads to serious performance deterioration. To overcome this limitation, in this paper, we build a connection between noisy samples and hard samples in the framework of self-paced learning, and propose a \underline{B}alanced \underline{S}elf-\underline{P}aced \underline{M}etric \underline{L}earning (BSPML) algorithm with a denoising multi-similarity formulation, where noisy samples are treated as extremely hard samples and adaptively excluded from the model training by sample weighting. Especially, due to the pairwise relationship and a new balance regularization term, the sub-problem \emph{w.r.t.} sample weights is a nonconvex quadratic function. To efficiently solve this nonconvex quadratic problem, we propose a doubly stochastic projection coordinate gradient algorithm. Importantly, we theoretically prove the convergence not only for the doubly stochastic projection coordinate gradient algorithm, but also for our BSPML algorithm. Experimental results on several standard data sets demonstrate that our BSPML algorithm has better generalization ability and robustness than the state-of-the-art robust DML approaches.
