提出无需人工干预的深度学习模型鲁棒性评估方法
Approach to Finding a Robust Deep Learning Model
- 基于元算法设计模型选择机制，适用于各类模型
- 发现训练样本量、权重初始化和归纳偏置显著影响鲁棒性
- 适合自动化模型训练与可靠性验证场景
机器学习与人工智能应用的快速发展催生了大量模型训练需求。为降低对人工干预的依赖并确保预测可靠性，本文提出一种新型模型鲁棒性评估方法。该方法结合自研的模型选择元算法，可适配任意适合任务的机器学习模型。研究聚焦于由少量卷积层和全连接层构成的小型深度学习模型，采用常见优化器以提升可解释性与计算效率。在该框架下，系统考察了训练样本规模、模型权重初始化方式及归纳偏置对模型鲁棒性的影响。
原文摘要 · Abstract (English)
The rapid development of machine learning (ML) and artificial intelligence (AI) applications requires the training of large numbers of models. This growing demand highlights the importance of training models without human supervision, while ensuring that their predictions are reliable. In response to this need, we propose a novel approach for determining model robustness. This approach, supplemented with a proposed model selection algorithm designed as a meta-algorithm, is versatile and applicable to any machine learning model, provided that it is appropriate for the task at hand. This study demonstrates the application of our approach to evaluate the robustness of deep learning models. To this end, we study small models composed of a few convolutional and fully connected layers, using common optimizers due to their ease of interpretation and computational efficiency. Within this framework, we address the influence of training sample size, model weight initialization, and inductive bias on the robustness of deep learning models.
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