Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness

Longwei Wang; Xueqian Li; Zheng Zhang

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness

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arxiv 2412.07022 v1 pith:MQZVX3O6 submitted 2024-12-09 cs.CV cs.AI

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness

Longwei Wang , Xueqian Li , Zheng Zhang This is my paper

classification cs.CV cs.AI

keywords ensembleconvolutionaldensedensenetneuralarchitecturecross-connecteddcc-ecnn

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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.

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