Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation

• URL: http://arxiv.org/abs/2107.02629v1
• Date: Tue, 6 Jul 2021 14:08:54 GMT
• Title: Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation
• Authors: Yufei Wang, Haoliang Li, Lap-pui Chau, Alex C. Kot
• Abstract summary: Lack of generalization capability in the absence of sufficient and representative data is one of the challenges that hinder their practical application. We propose a simple, effective, and plug-and-play training strategy named Knowledge Distillation for Domain Generalization (KDDG) We find that both the richer dark knowledge" from the teacher network, as well as the gradient filter we proposed, can reduce the difficulty of learning the mapping.
• Score: 65.79387438988554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Though convolutional neural networks are widely used in different tasks, lack of generalization capability in the absence of sufficient and representative data is one of the challenges that hinder their practical application. In this paper, we propose a simple, effective, and plug-and-play training strategy named Knowledge Distillation for Domain Generalization (KDDG) which is built upon a knowledge distillation framework with the gradient filter as a novel regularization term. We find that both the ``richer dark knowledge" from the teacher network, as well as the gradient filter we proposed, can reduce the difficulty of learning the mapping which further improves the generalization ability of the model. We also conduct experiments extensively to show that our framework can significantly improve the generalization capability of deep neural networks in different tasks including image classification, segmentation, reinforcement learning by comparing our method with existing state-of-the-art domain generalization techniques. Last but not the least, we propose to adopt two metrics to analyze our proposed method in order to better understand how our proposed method benefits the generalization capability of deep neural networks.

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