Revolutionizing neural network efficiency: introducing FPAC for filter pruning via attention consistency.

Autor: Mana, Suja Cherukullapurath, Rajesh, Sudha, Governor, Kalaiarasi, Chandrasekaran, Hemalatha, Murugesan, Kanipriya
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Zdroj: Neural Computing & Applications; Jan2024, Vol. 36 Issue 2, p639-652, 14p
Abstrakt: The process of feature mapping is used to represent features on a map with their corresponding properties. The features are displayed visually and their associated information is made available. Various abstraction approaches, including alignment approaches, are introduced to reduce the computational complexity. However, previous works based on standard treatments of feature maps often suffer from the negative effects of noise and background details. To solve this problem, we present a simple and effective implementation approach for Filter Pruning via Attention Consistency (FPAC), which determines a revolutionary filter pruning mechanism. Feature maps that concentrate on a single layer are inconsistent, which can affect the spatial dimension. The feature map with minimum consistency is less significant and is experimentally demonstrated. This study presents a novel layer-wise pruning technique using the Aphid Ant Mutualism (AAM) algorithm, which considers the sensitivity of various convolutional network layers to model inference and sets the optimal pruning ratio. The accuracy of the compressed model is enhanced by eliminating high redundancy through pruning correlated filters. The performance of FPAC is confirmed through the Caltech 256 image dataset. With VGG-16 on the Caltech 256 image dataset, the classification accuracy is enhanced from 93.96 to 94.03%. With ResNet-50 on the Caltech 256 image dataset, 45% FLOPs are achieved with an accuracy loss of only 0.53%. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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