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HODEC: Towards Efficient High-Order DEcomposed Convolutional Neural Networks
High-order decomposition is a widely used model compression approach towards compact convolutional neural networks (CNNs).
SPARK: co-exploring model SPArsity and low-RanKness for compact neural networks
Based on the observations and outcomes from our analysis, we then propose SPARK, a unified DNN compression framework that can simultaneously capture model SPArsity and low-RanKness in an efficient way.
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