Knowledge Extraction with No Observable Data

NeurIPS 2019 Jaemin YooMinyong ChoTaebum KimU Kang

Knowledge distillation is to transfer the knowledge of a large neural network into a smaller one and has been shown to be effective especially when the amount of training data is limited or the size of the student model is very small. To transfer the knowledge, it is essential to observe the data that have been used to train the network since its knowledge is concentrated on a narrow manifold rather than the whole input space... (read more)

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