I-CNet: Leveraging Involution and Convolution for Image Classification

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.