论文标题
SSDL：自我监督的领域学习以改善面部识别
SSDL: Self-Supervised Domain Learning for Improved Face Recognition
论文作者
论文摘要
由于照明，感测质量，运动模糊和等方面的差异，在不受约束的环境中的面部识别是具有挑战性的。在不同的条件下，个人的面部外观可能会大大变化，从而在火车（来源）和不同的测试（目标）数据之间造成差距。域间隙可能导致直接知识转移从源到目标的绩效水平降低。尽管对域进行了微调，但特定数据可能是一个有效的解决方案，但为所有域收集和注释数据非常昂贵。为此，我们提出了一种自我监管的域学习（SSDL）方案，该方案在未标记数据中挖掘的三胞胎上训练。有效歧视学习的关键因素是选择信息丰富的三胞胎。在最自信的预测的基础上，我们遵循了替代三胞胎开采和自学学习的“易于坚强”的计划。对四个不同基准测试的综合实验表明，SSDL在不同的领域上很好地概括了。
Face recognition in unconstrained environments is challenging due to variations in illumination, quality of sensing, motion blur and etc. An individual's face appearance can vary drastically under different conditions creating a gap between train (source) and varying test (target) data. The domain gap could cause decreased performance levels in direct knowledge transfer from source to target. Despite fine-tuning with domain specific data could be an effective solution, collecting and annotating data for all domains is extremely expensive. To this end, we propose a self-supervised domain learning (SSDL) scheme that trains on triplets mined from unlabelled data. A key factor in effective discriminative learning, is selecting informative triplets. Building on most confident predictions, we follow an "easy-to-hard" scheme of alternate triplet mining and self-learning. Comprehensive experiments on four different benchmarks show that SSDL generalizes well on different domains.
