Image Classification Network Based on Complementary Decay Learning

YUAN Heng; TIAN Wenyue; ZHANG Shengchong

doi:10.11999/JEIT260751

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YUAN Heng, TIAN Wenyue, ZHANG Shengchong. Image Classification Network Based on Complementary Decay Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260751

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YUAN Heng, TIAN Wenyue, ZHANG Shengchong. Image Classification Network Based on Complementary Decay Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260751

YUAN Heng, TIAN Wenyue, ZHANG Shengchong. Image Classification Network Based on Complementary Decay Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260751

Citation:

YUAN Heng, TIAN Wenyue, ZHANG Shengchong. Image Classification Network Based on Complementary Decay Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260751

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Image Classification Network Based on Complementary Decay Learning

doi: 10.11999/JEIT260751 cstr: 32379.14.JEIT260751

1.
Software College of Liaoning Technical University, Liaoning Huludao 125105, China
2.
Key Laboratory of Optoelectronic Information Control and Security Technology, Tianjin 300308, China

Funds: National Natural Science Foundation of China (61601213),, Natural Science Foundation of Liaoning Province (20170540426), Key Foundation of Education Department of Liaoning Province (LJYL049)

Received Date: 2026-04-10
Accepted Date: 2026-08-26
Rev Recd Date: 2026-08-26

Available Online: 2026-09-02

Abstract

Abstract

Objective Image classification depends on complete and discriminative feature representations. Existing convolutional neural networks usually enhance positive and high-amplitude responses through activation functions and attention mechanisms. However, excessive reliance on dominant positive responses may shift attention from the whole object to local salient regions, while negative and low-amplitude responses containing edge, texture, and foreground-background transition information are often weakened. To address this problem, a Complementary Decay Learning Network (CDLNet) is proposed to suppress high-response dominance and preserve complementary feature information. Methods Inspired by the signal attenuation mechanism of the biological visual system, CDLNet introduces complementary decay learning into a residual network. The Spatial Complementary Decay (SCD) module divides features into positive-response, negative-response, and global-response branches, and applies differentiated decay to preserve salient regions, boundary details, and contextual information (Fig.2). The Channel Complementary Decay (CCD) module attenuates high-response channels while retaining middle- and low-response channels, thereby reducing channel dominance and promoting cooperative channel representation (Fig.3). The Complementary Decay Attention (CDA) module integrates SCD and CCD in parallel and is embedded into ResNet residual blocks to jointly regulate spatial structures and channel semantics (Fig.4, Fig.6). Results and Discussions Experiments are conducted on CIFAR-10, CIFAR-100, SVHN, Imagenette, Imagewoof, and ImageNet datasets. CDLNet achieves classification accuracies of 96.68%, 81.81%, 97.38%, 92.45%, 85.46%, and 62.88%, respectively. Compared with ResNet-34 and representative classification networks, CDLNet obtains higher accuracy on multiple datasets (Table 5, Table 6). Ablation experiments demonstrate that removing either SCD or CCD reduces classification accuracy, indicating that spatial and channel complementary decay both contribute to feature regulation (Table 4, Table 5). Visualization results show that CDLNet can enhance target regions, preserve structural details, and suppress irrelevant background responses (Fig.1, Fig.12). Although CDA increases parameters and computation, the accuracy improvement shows a reasonable balance between performance and complexity (Table 3). Conclusions CDLNet introduces spatial and channel complementary decay mechanisms into residual networks. By suppressing excessive high-response dominance and preserving negative-response and low-amplitude information, the proposed method improves the completeness and balance of feature representations, alleviates attention drift, and enhances image classification performance. Future work will further optimize the decay strategy and model complexity to improve efficiency and generalization.
- Image classification,
- Residual network,
- Complementary decay attention,
- Complementary decay learning mechanism

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References(35)

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