论文标题
基于篮子的软马克斯
Basket-based Softmax
论文作者
论文摘要
基于软马克斯的损失已在各种任务（例如面部识别和重新识别）上实现了最先进的表现。但是，这些方法高度依赖于带有全球标签的清洁数据集，这限制了它们在许多现实世界中的用法。一个重要的原因是，从各种时间和空间场景中合并和组织数据集通常是不现实的，因为可以引入嘈杂的标签，并且需要指数增加资源。为了解决这个问题，我们提出了一种新颖的挖掘培训策略，称为基于篮子的软马克斯（BBS）及其并行版本，以端到端的方式有效地在多个数据集上训练模型。具体而言，对于每个培训样本，我们同时采用相似性得分作为从其他数据集中挖掘负面类别的线索，并动态添加它们以帮助学习判别特征。在实验上，我们通过模拟和现实世界的数据集证明了BBS对面部识别和重新识别任务的效率和优势。
Softmax-based losses have achieved state-of-the-art performances on various tasks such as face recognition and re-identification. However, these methods highly relied on clean datasets with global labels, which limits their usage in many real-world applications. An important reason is that merging and organizing datasets from various temporal and spatial scenarios is usually not realistic, as noisy labels can be introduced and exponential-increasing resources are required. To address this issue, we propose a novel mining-during-training strategy called Basket-based Softmax (BBS) as well as its parallel version to effectively train models on multiple datasets in an end-to-end fashion. Specifically, for each training sample, we simultaneously adopt similarity scores as the clue to mining negative classes from other datasets, and dynamically add them to assist the learning of discriminative features. Experimentally, we demonstrate the efficiency and superiority of the BBS on the tasks of face recognition and re-identification, with both simulated and real-world datasets.
