Results 11 to 20 of about 14,609 (251)
Evolutionary Multi-Objective One-Shot Filter Pruning for Designing Lightweight Convolutional Neural Network [PDF]
Sensors, 2021 Deep neural networks have achieved significant development and wide applications for their amazing performance. However, their complex structure, high computation and storage resource limit their applications in mobile or embedding devices such as sensor
Tao Wu +4 more
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MLPruner: pruning convolutional neural networks with automatic mask learning [PDF]
PeerJ Computer ScienceIn recent years, filter pruning has been recognized as an indispensable technique for mitigating the significant computational complexity and parameter burden associated with deep convolutional neural networks (CNNs).
Sihan Chen, Ying Zhao
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PCA driven mixed filter pruning for efficient convNets. [PDF]
PLoS ONE, 2022 Deployment of the deep neural networks (DNNs) on resource-constrained devices is a challenging task due to their limited memory and computational power.
Waqas Ahmed +3 more
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An accuracy-aware extension to lrp-based pruning for CNNs to prevent cascading accuracy degradation in data-scarce transfer learning [PDF]
Scientific ReportsConvolutional Neural Networks (CNNs) pre-trained on large-scale datasets such as ImageNet are widely used as feature extractors to construct high-accuracy classification models from scarce data for specific tasks.
Daisuke Yasui +2 more
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A Pruning Method Based on Feature Map Similarity Score
Big Data and Cognitive Computing, 2023 As the number of layers of deep learning models increases, the number of parameters and computation increases, making it difficult to deploy on edge devices. Pruning has the potential to significantly reduce the number of parameters and computations in a
Jihua Cui +3 more
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Applied Sciences, 2022 The superior performance of the recent deep learning models comes at the cost of a significant increase in computational complexity, memory use, and power consumption.
Deepak Ghimire, Seong-Heum Kim
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Towards Optimal Filter Pruning with Balanced Performance and Pruning Speed [PDF]
, 2021 Filter pruning has drawn more attention since resource constrained platform requires more compact model for deployment. However, current pruning methods suffer either from the inferior performance of one-shot methods, or the expensive time cost of iterative training methods.
Dong Li +4 more
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Applied Sciences, 2022 Deep neural networks (DNNs) have achieved great success in the field of computer vision. The high requirements for memory and storage by DNNs make it difficult to apply them to mobile or embedded devices. Therefore, compression and structure optimization
Yihao Feng +4 more
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Filter Pruning Via Filters Similarity in Consecutive Layers
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023 Accepted by ICASSP 2023 (oral)
Xiaorui Wang +5 more
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Model pruning based on filter similarity for edge device deployment
Frontiers in Neurorobotics, 2023 Filter pruning is widely used for inference acceleration and compatibility with off-the-shelf hardware devices. Some filter pruning methods have proposed various criteria to approximate the importance of filters, and then sort the filters globally or ...
Tingting Wu +9 more
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