Abstract
The existence of noisy labels in real-world data negatively impacts the performance of deep learning models. Although much research effort has been devoted to improving the robustness towards noisy labels in classification tasks, the problem of noisy labels in deep metric learning (DML) remains under-explored. Existing noisy label learning methods designed for DML mainly discard suspicious noisy samples, resulting in a waste of the training data. To address this issue, we propose a noise-robust DML framework with SubGroup-based Positive-pair Selection (SGPS), which constructs reliable positive pairs for noisy samples to enhance the sample utilization. Specifically, SGPS first effectively identifies clean and noisy samples by a probability-based clean sample selectionstrategy. To further utilize the remaining noisy samples, we discover their potential similar samples based on the subgroup information given by a subgroup generation module and then aggregate them into informative positive prototypes for each noisy sample via a positive prototype generation module. Afterward, a new contrastive loss is tailored for the noisy samples with their selected positive pairs. SGPS can be easily integrated into the training process of existing pair-wise DML tasks, like image retrieval and face recognition. Extensive experiments on multiple synthetic and real-world large-scale label noise datasets demonstrate the effectiveness of our proposed method. Without any bells and whistles, our SGPS framework outperforms the state-of-the-art noisy label DML methods. Code is available at \url{this https URL}.
Abstract (translated)
真实世界数据中存在的嘈杂标签会对深度学习模型的性能产生负面影响。尽管在提高分类任务对噪声标签鲁棒性方面投入了大量的研究努力，但关于深度度量学习（DML）中的噪声标签问题仍缺乏深入探索。现有的针对DML设计的噪声标签学习方法主要通过丢弃可疑的噪声样本来处理，导致了训练数据的浪费。为了解决这一问题，我们提出了一种基于子组正对选择（SGPS）的抗噪鲁棒深度度量学习框架，该框架构建了可靠的正对以增强噪声样本的使用效率。具体来说，SGPS首先通过一种概率基础的干净样本选择策略有效地识别干净和噪声样本。为进一步利用剩余的噪声样本，我们基于子组生成模块提供的子组信息发现其潜在相似样本，并通过正原型生成模块将其聚合为每个噪声样本的信息丰富正原型。随后，我们为带有选定正对的噪声样本定制了一种新的对比损失函数。SGPS可以轻松集成到现有的成对DML任务（如图像检索和人脸识别）训练过程中。在多个合成和真实世界的大规模标签噪声数据集上的广泛实验表明了我们提出的方法的有效性。无需任何复杂调整，我们的SGPS框架就超过了当前最先进的噪声标签DML方法的表现。代码可在[\url{this https URL}]获取。
URL
https://arxiv.org/abs/2501.11063
