No abstract

Knowledge transfer from large teacher models to smaller student models has recently been studied for metric learning, focusing on fine-grained classification. In this work, focusing on instance-level image retrieval, we study an asymmetric testing task, where the database is represented by the teacher and queries by the student. Inspired by this task, we introduce asymmetric metric learning, a novel paradigm of using asymmetric representations at training. This acts as a simple combination of knowledge transfer with the original metric learning task. We systematically evaluate different teacher and student models, metric learning and knowledge transfer loss functions on the new asymmetric testing as well as the standard symmetric testing task, where database and queries are represented by the same model. We find that plain regression is surprisingly effective compared to more complex knowledge transfer mechanisms, working best in asymmetric testing. Interestingly, our asymmetric metric learning approach works best in symmetric testing, allowing the student to even outperform the teacher.

Pattern recognition and machine learning methods provide an attractive approach for building decision support systems. Classification trees are frequently used algorithms for such tasks owing to their intuitive structure and effectiveness. It has been shown that for complex medical data, combining a number of base classifiers improves their overall accuracy. Classification tree ensembles have a certain number of free parameters to set, which can significantly affect their performance. In recent years such ensembles were often used by practitioners without a mathematical background (e.g. physicians), who may be unaware of how to obtain the optimal settings. Therefore, it is difficult for them to choose the satisfactory properties, while in most of the cases the default parameters proposed for them are not necessarily the most efficient. The aim of this article is to ascertain which types of combined tree classifiers give the best performance for medical decision support and which parameters should be chosen for them. A set of rules for end-users on how to tune their ensembles is proposed.