2021
DOI: 10.3390/s21103464
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Implementation of Lightweight Convolutional Neural Networks via Layer-Wise Differentiable Compression

Abstract: Convolutional neural networks (CNNs) have achieved significant breakthroughs in various domains, such as natural language processing (NLP), and computer vision. However, performance improvement is often accompanied by large model size and computation costs, which make it not suitable for resource-constrained devices. Consequently, there is an urgent need to compress CNNs, so as to reduce model size and computation costs. This paper proposes a layer-wise differentiable compression (LWDC) algorithm for compressi… Show more

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Cited by 5 publications
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“…In recent years, most research on facial beauty prediction has been based on deep learning methods [9]. The development of deep learning architecture has been driven by the strength and adaptability of these algorithms, particularly convolutional neural networks (CNNs) [10].…”
Section: Introductionmentioning
confidence: 99%
“…In recent years, most research on facial beauty prediction has been based on deep learning methods [9]. The development of deep learning architecture has been driven by the strength and adaptability of these algorithms, particularly convolutional neural networks (CNNs) [10].…”
Section: Introductionmentioning
confidence: 99%