Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909422030159872 |
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| author | Wang, Longwei Li, Xueqian Zhang, Zheng |
| author_facet | Wang, Longwei Li, Xueqian Zhang, Zheng |
| contents | The resilience of convolutional neural networks against input variations and adversarial attacks remains a significant challenge in image recognition tasks. Motivated by the need for more robust and reliable image recognition systems, we propose the Dense Cross-Connected Ensemble Convolutional Neural Network (DCC-ECNN). This novel architecture integrates the dense connectivity principle of DenseNet with the ensemble learning strategy, incorporating intermediate cross-connections between different DenseNet paths to facilitate extensive feature sharing and integration. The DCC-ECNN architecture leverages DenseNet's efficient parameter usage and depth while benefiting from the robustness of ensemble learning, ensuring a richer and more resilient feature representation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07022 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness Wang, Longwei Li, Xueqian Zhang, Zheng Computer Vision and Pattern Recognition Artificial Intelligence The resilience of convolutional neural networks against input variations and adversarial attacks remains a significant challenge in image recognition tasks. Motivated by the need for more robust and reliable image recognition systems, we propose the Dense Cross-Connected Ensemble Convolutional Neural Network (DCC-ECNN). This novel architecture integrates the dense connectivity principle of DenseNet with the ensemble learning strategy, incorporating intermediate cross-connections between different DenseNet paths to facilitate extensive feature sharing and integration. The DCC-ECNN architecture leverages DenseNet's efficient parameter usage and depth while benefiting from the robustness of ensemble learning, ensuring a richer and more resilient feature representation. |
| title | Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2412.07022 |