Active weighted mapping-based residual convolutional neural network for image classification

In visual recognition, the key to the performance improvement of ResNet is the success in establishing the stack of deep sequential convolutional layers using identical mapping by a shortcut connection. It results in multiple paths of data flow under a network and the paths are merged with the equal...

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Bibliographic Details
Published in:Multimedia tools and applications 2021-10, Vol.80 (24), p.33139-33153
Main Authors: Jung, Hyoungho, Lee, Ryong, Lee, Sang-Hwan, Hwang, Wonjun
Format: Article
Language:English
Subjects:
Artificial neural networks
Computer Communication Networks
Computer networks
Computer Science
Data Structures and Information Theory
Image classification
Mapping
Multimedia Information Systems
Neural networks
Object recognition
Special Purpose and Application-Based Systems
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Weights & measures
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Summary:In visual recognition, the key to the performance improvement of ResNet is the success in establishing the stack of deep sequential convolutional layers using identical mapping by a shortcut connection. It results in multiple paths of data flow under a network and the paths are merged with the equal weights. However, it is questionable whether it is correct to use the fixed and predefined weights at the mapping units of all paths. In this paper, we introduce the active weighted mapping method which infers proper weight values based on the characteristic of input data on the fly. The weight values of each mapping unit are not fixed but changed as the input image is changed, and the most proper weight values for each mapping unit are derived according to the input image. For this purpose, channel-wise information is embedded from both the shortcut connection and convolutional block, and then the fully connected layers are used to estimate the weight values for the mapping units. We train the backbone network and the proposed module alternately for a more stable learning of the proposed method. Results of the extensive experiments show that the proposed method works successfully on the various backbone architectures from ResNet to DenseNet. We also verify the superiority and generality of the proposed method on various datasets in comparison with the baseline.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-020-09808-3