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A feature compression method based on similarity matching
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AI
DOI：10.1016/j.displa.2024.102728.png)
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Abstract
En 中文
In collaborative intelligent applications, it is attractive to split the neural network into two parts. The front part is deployed on an edge device and the remaining part on the cloud side. It is a promising approach to compress the intermediate features for high efficiency. In this paper, a novel feature compression method based on feature similarity matching is presented. According to the statistical characteristics of features, the noninformative features that are not sensitive to the compression distortion are determined. The similarity degree between the noninformative features and other features is calculated and the feature with the high similarity degree will be removed in the encoder and replaced by the noninformative features in the decoder. In addition, transformation is introduced to transform the features in a compact form to reduce the statistical redundancy. Without loss of generality, the image classification is taken as intelligent task to validate the effectiveness of the proposed method. Experimental results show that the proposed method can achieve higher compression efficiency in various bit rate. In comparison with different feature compression algorithms, at least 24% bitrate saving is obtained.
Keywords:
Neural networks
Feature compression
Similarity matching
Discrete cosine transformation
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