Knowledge Transfer Graph for Deep Collaborative Learning
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We propose Deep Collaborative Learning (DCL), which is a method that incorporates Knowledge Distillation and Deep Mutual Learning, and represents graph using a more generalized knowledge transfer method. DCL is represented by a directional graph where each model is represented by a node, and the propagation of knowledge from the source node to the target node is represented by edges. In DCL, a hyperparameter search can be used to search for an optimal knowledge transfer graph. We also propose four types of gate structure to control the propagation of gradients through the network for edges. When searching a knowledge transfer graph, optimization is performed to maximize the recognition rate of optimization target node using collaborative learning network types and gate types as hyperparameters. Using the CIFAR-100 dataset to search for an optimal knowledge transfer graph structure, we obtained a graph structure learning method that combines Knowledge Distillation with Deep Mutual Learning. Also, in experiments with the CIFAR-10, CIFAR-100 and Tiny-ImageNet datasets, we achieved a significant improvement in accuracy without increasing the network parameters beyond the vanilla model. We also show that an optimized graph can be transferred to a different dataset.
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