Knowledge distillation most benefits intermediate-complexity students for time series classification, cutting parameters sharply while matching teacher accuracy across FCN, Inception, and ConvTran on UCR.
IEEE Trans Pattern Anal Mach Intell 350(8):1798–1828
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Enhancing deep learning models for time series classification via knowledge distillation
Knowledge distillation most benefits intermediate-complexity students for time series classification, cutting parameters sharply while matching teacher accuracy across FCN, Inception, and ConvTran on UCR.