#5812. Progressive kernel pruning with saliency mapping of input-output channels

July 2026publication date
Proposal available till 15-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Cognitive Neuroscience;
Computer Science Applications;
Artificial Intelligence;
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Abstract:
As the smallest structural unit of feature mapping, the convolution kernel in a deep convolution neural networks (DCNN) convolutional layer is responsible for the input channel features to output channel features. A specific convolution kernel belongs to a specific group from the perspective of the input channel, and it belongs to a specific filter from the perspective of the output channel. If the input and output channels are simultaneously considered in the pruning process, the performance of the pruning model can be further improved. This paper proposes progressive kernel pruning with salient mapping of input-output channels, introduces the concept of input-output channel saliency and defines single-port salient mapping channels and dual-port salient mapping channels.
Keywords:
Acceleration; Compression; Hybrid norm sparse index; Progressive kernel pruning; Saliency mapping

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