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：本文提出了一种在训练过程中动态扩展卷积神经网络的新方法，旨在满足对高效且可持续深度学习模型日益增长的需求。我们的方法借鉴了自扩展神经网络领域的开创性工作，采用自然扩展评分作为扩展准则，以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与任务特定需求精准匹配。该方法的一个显著优势在于其环保特性——它避免了训练多个不同规模模型的必要性。我们采用单模型动态扩展策略，使研究者能够从同一训练过程中提取不同复杂度级别的检查点，这不仅有效降低了计算资源消耗和能源使用，还通过提供多样化的模型复杂度加速了开发周期。我们在CIFAR-10数据集上评估了该方法，实验结果验证了其有效性，证明动态添加层不仅能维持、更能提升CNN性能，充分彰显了我们扩展准则的优越性。这一方法在开发自适应、可扩展且环境友好的神经网络架构方面取得了重要进展，有效应对了深度学习领域的核心挑战。