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Feature Fusion for Online Mutual Knowledge Distillation by Kim, Jangho; Hyun, Minsung; Chung, Inseop; Kwak, Nojun is a scholarly article available to read on EtoBox.

What is Feature Fusion for Online Mutual Knowledge Distillation about?

We propose a learning framework named Feature Fusion Learning (FFL) that efficiently trains a powerful classifier through a fusion module which combines the feature maps generated from parallel neural networks. Specifically, we train a number of parallel neural networks as sub-networks, then we combine the feature maps from each sub-network using a fusion module to create a more meaningful feature map. The fused feature map is passed into the fused classifier for overall classification. Unlike existing feature fusion methods, in our framework, an ensemble of sub-network classifiers transfers its knowledge to the fused classifier and then the fused classifier delivers its knowledge back to each sub-network, mutually teaching one another in an online-knowledge distillation manner. This mutually teaching system not only improves the performance of the fused classifier but also obtains performance gain in each sub-network. Moreover, our model is more beneficial because different types of network can be used for each sub-network. We have performed a variety of experiments on multiple datasets such as CIFAR-10, CIFAR-100 and ImageNet and proved that our method is more effective than othe

Author

Kim, Jangho; Hyun, Minsung; Chung, Inseop; Kwak, Nojun

Published

2019

Language

EN