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<p>Deep neural networks have been widely applied in various fields—such as image recognition, natural language processing, and robotics—achieving remarkable success. Ensemble learning is a well-established technique to improve the accuracy and generalization of neural networks by aggregating multiple models. Unlike conventional ensemble methods that combine the final outputs of independent models, this paper introduces a novel sub-network-level ensemble approach which integrates neurons, layers, and blocks within a single network. The proposed method leverages a checkpoint-based aggregation mechanism to combine multiple snapshots of a model during training, thereby ensuring greater diversity and reducing overfitting. Additionally, a regularized loss function is introduced to enhance diversity at different levels of the network, leading to improved generalization. Experimental results on standard benchmark datasets—including CIFAR-10, CIFAR-100, MNIST, and Fashion-MNIST—demonstrate that the proposed method outperforms traditional ensemble techniques, achieving accuracy improvements ranging from 0.08 to 7.05%. Furthermore, a computational complexity analysis confirms that the proposed approach maintains a balanced trade-off between accuracy and computational efficiency compared to existing methods.</p>

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A novel sub-network level ensemble deep neural network with a regularized loss function to improve prediction performance

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月8日

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  • Jalil Toosifar,
  • Yahya Forghani,
  • Seyyed Abed Hosseini,
  • Nasser Shoeibi

摘要

Deep neural networks have been widely applied in various fields—such as image recognition, natural language processing, and robotics—achieving remarkable success. Ensemble learning is a well-established technique to improve the accuracy and generalization of neural networks by aggregating multiple models. Unlike conventional ensemble methods that combine the final outputs of independent models, this paper introduces a novel sub-network-level ensemble approach which integrates neurons, layers, and blocks within a single network. The proposed method leverages a checkpoint-based aggregation mechanism to combine multiple snapshots of a model during training, thereby ensuring greater diversity and reducing overfitting. Additionally, a regularized loss function is introduced to enhance diversity at different levels of the network, leading to improved generalization. Experimental results on standard benchmark datasets—including CIFAR-10, CIFAR-100, MNIST, and Fashion-MNIST—demonstrate that the proposed method outperforms traditional ensemble techniques, achieving accuracy improvements ranging from 0.08 to 7.05%. Furthermore, a computational complexity analysis confirms that the proposed approach maintains a balanced trade-off between accuracy and computational efficiency compared to existing methods.