论文标题
描述师蒸馏：一个教师规范化的框架，用于学习本地描述符
Descriptor Distillation: a Teacher-Student-Regularized Framework for Learning Local Descriptors
论文作者
论文摘要
在计算机视觉中学习快速而判别的补丁描述是一个艰巨的话题。最近，许多现有的作品通过最大程度地减少三胞胎损失（或其变体）来培训各种描述符学习网络，这有望降低每对正对之间的距离并增加每个负对之间的距离。但是，由于网络优化器与本地解决方案的非完美收敛性，必须降低这种期望。在解决这个问题和开放的计算速度问题时，我们为当地描述符学习（称为Desdis）提出了一个描述剂蒸馏框架，该框架称为Desdis，其中学生模型从预先训练的教师模型中获得了知识，并通过设计的教师学生的规律员进一步增强了知识。这个教师学生的正规化程序是为了限制教师模型的正（也是负）相似性与学生模型的相似性之间的差异，并且从理论上我们证明，可以通过最大程度地减少三胞胎损失的加权组合来训练更有效的学生模型，而不是通过将三胞胎损失最小化的训练来培训。在拟议的desdis下，许多现有的描述符网络可以嵌入为教师模型，因此，可以得出同等重量和轻巧的学生模型，这可以以准确的或速度的速度优于他们的老师。 3个公共数据集的实验结果表明，通过利用三个典型的描述符学习网络作为教师模型，从拟议的DESDIS框架中得出的均等学生模型可以比其教师和其他几种比较方法取得更好的表现。此外，在相似的贴片验证性能下，派生的轻重量模型可以达到8次甚至更快的速度
Learning a fast and discriminative patch descriptor is a challenging topic in computer vision. Recently, many existing works focus on training various descriptor learning networks by minimizing a triplet loss (or its variants), which is expected to decrease the distance between each positive pair and increase the distance between each negative pair. However, such an expectation has to be lowered due to the non-perfect convergence of network optimizer to a local solution. Addressing this problem and the open computational speed problem, we propose a Descriptor Distillation framework for local descriptor learning, called DesDis, where a student model gains knowledge from a pre-trained teacher model, and it is further enhanced via a designed teacher-student regularizer. This teacher-student regularizer is to constrain the difference between the positive (also negative) pair similarity from the teacher model and that from the student model, and we theoretically prove that a more effective student model could be trained by minimizing a weighted combination of the triplet loss and this regularizer, than its teacher which is trained by minimizing the triplet loss singly. Under the proposed DesDis, many existing descriptor networks could be embedded as the teacher model, and accordingly, both equal-weight and light-weight student models could be derived, which outperform their teacher in either accuracy or speed. Experimental results on 3 public datasets demonstrate that the equal-weight student models, derived from the proposed DesDis framework by utilizing three typical descriptor learning networks as teacher models, could achieve significantly better performances than their teachers and several other comparative methods. In addition, the derived light-weight models could achieve 8 times or even faster speeds than the comparative methods under similar patch verification performances
