In this paper, we present a novel method for dynamically expanding Convolutional Neural Networks (CNNs) during training, aimed at meeting the increasing demand for efficient and sustainable deep learning models. Our approach, drawing from the seminal work on Self-Expanding Neural Networks (SENN), employs a natural expansion score as an expansion criteria to address the common issue of over-parameterization in deep convolutional neural networks, thereby ensuring that the model's complexity is finely tuned to the task's specific needs. A significant benefit of this method is its eco-friendly nature, as it obviates the necessity of training multiple models of different sizes. We employ a strategy where a single model is dynamically expanded, facilitating the extraction of checkpoints at various complexity levels, effectively reducing computational resource use and energy consumption while also expediting the development cycle by offering diverse model complexities from a single training session. We evaluate our method on the CIFAR-10 dataset and our experimental results validate this approach, demonstrating that dynamically adding layers not only maintains but also improves CNN performance, underscoring the effectiveness of our expansion criteria. This approach marks a considerable advancement in developing adaptive, scalable, and environmentally considerate neural network architectures, addressing key challenges in the field of deep learning.

翻译：本文提出一种在训练过程中动态扩张卷积神经网络的创新方法，旨在满足对高效且可持续深度学习模型日益增长的需求。该方法借鉴自扩张神经网络（SENN）的开创性工作，采用自然扩张评分作为扩展准则，解决深度卷积神经网络中常见的过参数化问题，从而确保模型复杂度精确适配任务的特定需求。该方法的重要优势在于其生态友好特性，无需训练多个不同规模的模型。我们采用单模型动态扩张策略，可提取不同复杂度级别的模型检查点，在有效降低计算资源使用和能耗的同时，通过单次训练提供多种模型复杂度方案以加速开发周期。我们在CIFAR-10数据集上进行评估，实验结果验证了该方法的有效性：动态添加层不仅能维持CNN性能，更可提升其表现，充分证明了扩张准则的优越性。该方法在开发自适应、可扩展且环境友好的神经网络架构方面取得重要进展，有效应对深度学习领域的核心挑战。