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I-CNet: Leveraging Involution and Convolution for Image Classification
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DOI：10.1109/ACCESS.2021.3139464.png)
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Abstract
En 中文
Convolution is widely adapted in deep learning models on image classification tasks for extracting hidden spatial-domain representations. However, as convolution is channel-specific, the potential cross-channel correlations in images are often neglected. This paper proposes a novel model, namely I-CNet, which leverages involution and convolution for improving the accuracy of image classification tasks, by extracting feature representations on both channel domain and spatial domain. The proposed I-CNet has been experimented on three image classification datasets. The experimental results show that involution component employed in I-CNet model can effectively represent cross-channel features in images and I-CNet is superior to other comparatives with higher classification accuracy achieved on all the three datasets.
Keywords:
Convolution
Transfer learning
Kernel
Feature extraction
Training
Task analysis
Optimization
Image classification
involution
convolution
hybrid architecture
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W