Unifying Heterogeneous Classifiers With Distillation

By Jayakorn Vongkulbhisal, Phongtharin Vinayavekhin, Marco Visentini Scarzanella. 2019

57 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Group Knowledge Transfer: Collaborative Training of Large CNNs on the Edge (2020, background): “Moreover, all teachers and students in distillation share the same dataset [38, 40, 41, 42], while in our setting each member (client) can only access its own independent dataset.”
Knowledge Distillation: A Survey (2020, background): “To avoid exposing the private data, multiple teachers access subsets of the sensitive or unlabelled data and supervise the student (Papernot et al., 2017; Vongkulbhisal et al., 2019).”
Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks (2020, background): “To tackle these problems, some recent works [64], [182], [191], [219], [231], [234] propose data distillation by unifying data sources from multiple teachers as illustrated in Fig.”
Adaptive multi-teacher multi-level knowledge distillation (2020, background): “Similarly, [26] trains a classifier in each source and unifying their classifications on an integrated label space.”
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge (2020, background): “Moreover, all teachers and students in distillation share the same dataset [42, 44, 45, 46], while in our setting each member (client) can only access its own independent dataset.”