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Image classification based on tensor network DenseNet model
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DOI：10.1007/s10489-024-05472-4.png)
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Image classification, the primary domain where deep neural networks significantly contribute to image analysis, requires a substantial amount of computer memory to train. This is particularly true in the fully connected layer, which accounts for 90% of the total memory. Moreover, the flattening operation could potentially result in the loss of the multi-linear structure of the image data. The tensor regression network, however, minimally impacts the performance of the neural network while achieving a high compression rate. This effectively mitigates the issue of large memory occupation in the neural network model. The DenseNet model, in particular, can alleviate the vanishing-gradient problem and strengthen feature propagation and outperform other existing networks. This article proposes a novel tensor network model that embeds the tensor regression layer into the DenseNet model. The framework of this tensor DenseNet model has been established, and its estimation procedure is developed. Tensor network model is applied to the classification of the following datasets: Fruits 360, 100 Sports Image, ASL Alphabet, and Mini-ImageNet. The experimental results indicate that the combination of the DenseNet model with the tensor regression layer not only conserves a significant amount of memory but also maintains a high accuracy of classification, compared with existing tensor network models.
Keyword:
Tensor decomposition
Tensor regression layer
Tensor Networks
Densely connected convolutional network
Image classification
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3.5
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