论文标题
通过教师学生网络进行广义属性预测的半监督学习
Semi-supervised Learning with a Teacher-student Network for Generalized Attribute Prediction
论文作者
论文摘要
本文介绍了一项关于半监督学习的研究，以解决视觉属性预测问题。在视力算法的许多应用中，对象的视觉属性的精确识别很重要，但仍然具有挑战性。这是因为定义属性的类层次结构是模棱两可的，因此训练数据不可避免地会遭受类不平衡和标签稀疏性的困扰，从而导致缺乏有效的注释。直观的解决方案是找到一种通过使用未标记的图像来有效地学习图像表示的方法。考虑到这一点，我们提出了一种由多任务学习和半监督学习的蒸馏而启发的多教老师圣训（MTSS）方法。我们的MTSS使用标签嵌入技术学习了特定于任务的领域专家，称为教师网络，并通过强迫模型模仿模型来模仿域专家学到的分布，从而学习了一个名为“学生网络”的统一模型。我们的实验表明，我们的方法不仅在各种基准上实现了时尚属性预测的竞争性能，而且还提高了看不见的域的稳健性和跨域的适应性。
This paper presents a study on semi-supervised learning to solve the visual attribute prediction problem. In many applications of vision algorithms, the precise recognition of visual attributes of objects is important but still challenging. This is because defining a class hierarchy of attributes is ambiguous, so training data inevitably suffer from class imbalance and label sparsity, leading to a lack of effective annotations. An intuitive solution is to find a method to effectively learn image representations by utilizing unlabeled images. With that in mind, we propose a multi-teacher-single-student (MTSS) approach inspired by the multi-task learning and the distillation of semi-supervised learning. Our MTSS learns task-specific domain experts called teacher networks using the label embedding technique and learns a unified model called a student network by forcing a model to mimic the distributions learned by domain experts. Our experiments demonstrate that our method not only achieves competitive performance on various benchmarks for fashion attribute prediction, but also improves robustness and cross-domain adaptability for unseen domains.
