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Leveraging Different Learning Styles for Improved Knowledge Distillation
Unlike conventional techniques that share the same type of knowledge with all networks, we propose to train individual networks with different forms of information to enhance the learning process.
Augmenting Knowledge Distillation With Peer-To-Peer Mutual Learning For Model Compression
Knowledge distillation (KD) is an effective model compression technique where a compact student network is taught to mimic the behavior of a complex and highly trained teacher network.
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