Results 11 to 20 of about 36,322 (258)
YOLO Pruning Algorithm Based on Parameter Subspace and Scaling Factor [PDF]
Jisuanji gongcheng, 2021 In order to ensure the normal operation of YOLO network on embedded devices,it is necessary to use pruning algorithm to simplify the filter to reduce the network storage space and the amount of calculation.
YANG Minjie, LIANG Yaling, DU Minghui
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Structured Pruning Algorithm with Adaptive Threshold Based on Gradient [PDF]
Jisuanji gongcheng, 2022 The network model needs to be compressed to reduce the number of model parameters and calculational cost to ensure the operation of the Deep Neural Network(DNN) model on edge equipment and real-time analysis. However, most existing pruning algorithms are
WANG Guodong, YE Jian, XIE Ying, QIAN Yueliang
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CoRR, 2023 Network pruning techniques, including weight pruning and filter pruning, reveal that most state-of-the-art neural networks can be accelerated without a significant performance drop. This work focuses on filter pruning which enables accelerated inference with any off-the-shelf deep learning library and hardware.
Xuanyu He +7 more
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Absorption Pruning of Deep Neural Network for Object Detection in Remote Sensing Imagery
Remote Sensing, 2022 In recent years, deep convolutional neural networks (DCNNs) have been widely used for object detection tasks in remote sensing images. However, the over-parametrization problem of DCNNs hinders their application in resource-constrained remote sensing ...
Jielei Wang +4 more
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Frontiers in Computational Neuroscience, 2021 Neural network pruning is critical to alleviating the high computational cost of deep neural networks on resource-limited devices. Conventional network pruning methods compress the network based on the hand-crafted rules with a pre-defined pruning ratio (
Lin Chen +3 more
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Meta-learning with Network Pruning [PDF]
, 2020 Meta-learning is a powerful paradigm for few-shot learning. Although with remarkable success witnessed in many applications, the existing optimization based meta-learning models with over-parameterized neural networks have been evidenced to ovetfit on training tasks.
Hongduan Tian +3 more
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Image Super-Resolution Reconstruction Algorithm Based on Sparse Neural Network [PDF]
Jisuanji gongcheng, 2022 Many deep learning-based image super-resolution reconstruction algorithms improve the overall feature expression ability of a network by extending the depth of the network.However, excessively extending the depth of the network causes the model to be ...
LI Haomin, LI Guangping
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A Pruning Method for Deep Convolutional Network Based on Heat Map Generation Metrics
Sensors, 2022 With the development of deep learning, researchers design deep network structures in order to extract rich high-level semantic information. Nowadays, most popular algorithms are designed based on the complexity of visible image features.
Wenli Zhang +4 more
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Pruning Method for Convolutional Neural Network Models Based on Sparse Regularization [PDF]
Jisuanji gongcheng, 2021 The existing pruning algorithms for Convolutional Neural Network(CNN) models exhibit a low accuracy in evaluating the importance of parameters by relying on their own parameter information, which would easily lead to mispruning and affect the performance
WEI Yue, CHEN Shichao, ZHU Fenghua, XIONG Gang
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To Prune or not to Prune: A Chaos-Causality Approach to Principled Pruning of Dense Neural Networks
CoRR, 2023 Reducing the size of a neural network (pruning) by removing weights without impacting its performance is an important problem for resource-constrained devices. In the past, pruning was typically accomplished by ranking or penalizing weights based on criteria like magnitude and removing low-ranked weights before retraining the remaining ones.
Rajan Sahu +4 more
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