This paper presents a novel holistic deep learning framework that simultaneously addresses the challenges of vulnerability to input perturbations, overparametrization, and performance instability from different train-validation splits. The proposed framework holistically improves accuracy, robustness, sparsity, and stability over standard deep learning models, as demonstrated by extensive experiments on both tabular and image data sets. The results are further validated by ablation experiments and SHAP value analysis, which reveal the interactions and trade-offs between the different evaluation metrics. To support practitioners applying our framework, we provide a prescriptive approach that offers recommendations for selecting an appropriate training loss function based on their specific objectives. All the code to reproduce the results can be found at https://github.com/kimvc7/HDL.

翻译：本文提出了一种新颖的整体深度学习框架，该框架同时应对输入扰动脆弱性、过参数化以及因不同训练-验证划分导致的性能不稳定等挑战。所提出的框架在表格数据和图像数据集上的大量实验表明，与传统深度学习模型相比，它在准确性、鲁棒性、稀疏性和稳定性方面实现了全面改进。消融实验和SHAP值分析进一步验证了研究结果，揭示了不同评估指标之间的相互影响和权衡。为帮助应用该框架的实践者，我们提供了一种基于具体目标选择合适训练损失函数的规范性方法。复现所有结果的代码可在https://github.com/kimvc7/HDL获取。