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논문 상세정보

Abstract

Considerable accuracy improvements in deep learning have recently been achieved in many applications that require large amounts of computation and expensive memory. However, recent advanced techniques for compacting and accelerating the deep learning model have been developed for deployment in lightweight devices with constrained resources. Lightweight deep learning techniques can be categorized into two schemes: lightweight deep learning algorithms (model simplification and efficient convolutional filters) in nature and transferring models into compact/small ones (model compression and knowledge distillation). In this report, we briefly summarize various lightweight deep learning techniques and possible research directions.

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참고문헌 (19)

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  18. 18. https://www.xnor.ai/ 
  19. 19. https://hyperconnect.com/ 

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