In this paper, we study the problem of unifying knowledge from a set of classifiers with different architectures and target classes into a single classifier, given only a generic set of unlabelled data. We call this problem Unifying Heterogeneous Classifiers (UHC). This problem is motivated by scenarios where data is collected from multiple sources, but the sources cannot share their data, e.g., due to privacy concerns, and only privately trained models can be shared. In addition, each source may not be able to gather data to train all classes due to data availability at each source, and may not be able to train the same classification model due to different computational resources. To tackle this problem, we propose a generalisation of knowledge distillation to merge HCs. We derive a probabilistic relation between the outputs of HCs and the probability over all classes. Based on this relation, we propose two classes of methods based on cross-entropy minimisation and matrix factorisation, which allow us to estimate soft labels over all classes from unlabelled samples and use them in lieu of ground truth labels to train a unified classifier. Our extensive experiments on ImageNet, LSUN, and Places365 datasets show that our approaches significantly outperform a naive extension of distillation and can achieve almost the same accuracy as classifiers that are trained in a centralised, supervised manner.

本文旨在研究如何将具有不同架构和目标类别的分类器的知识整合到一个单一的分类器中，提出了一种基于知识蒸馏的方法来合并异构分类器。根据概率关系，提出了两类方法：基于交叉熵最小化和矩阵分解，可以从未标记的样本中估计所有类别的软标签，并用它们来代替真实标签训练统一的分类器。在ImageNet，LSUN和Places365数据集上进行了大量实验，表明这些方法显著优于蒸馏的天真扩展，并且准确度几乎可以达到集中式监督训练分类器的水平。

异构分类器蒸馏化统一