Enhancing the generalisation abilities of neural networks (NNs) through integrating noise such as MixUp or Dropout during training has emerged as a powerful and adaptable technique. Despite the proven efficacy of noise in NN training, there is no consensus regarding which noise sources, types and placements yield maximal benefits in generalisation and confidence calibration. This study thoroughly explores diverse noise modalities to evaluate their impacts on NN's generalisation and calibration under in-distribution or out-of-distribution settings, paired with experiments investigating the metric landscapes of the learnt representations across a spectrum of NN architectures, tasks, and datasets. Our study shows that AugMix and weak augmentation exhibit cross-task effectiveness in computer vision, emphasising the need to tailor noise to specific domains. Our findings emphasise the efficacy of combining noises and successful hyperparameter transfer within a single domain but the difficulties in transferring the benefits to other domains. Furthermore, the study underscores the complexity of simultaneously optimising for both generalisation and calibration, emphasising the need for practitioners to carefully consider noise combinations and hyperparameter tuning for optimal performance in specific tasks and datasets.

翻译：通过整合MixUp或Dropout等噪声增强神经网络的泛化能力，已成为一种强大且灵活的训练技术。尽管噪声在神经网络训练中的有效性已得到证实，但对于哪些噪声源、类型和放置位置能在泛化与置信度校准中产生最大收益尚未达成共识。本研究系统探索了多种噪声模态，评估其在分布内或分布外场景下对神经网络泛化与校准的影响，并结合实验分析了跨不同架构、任务和数据集的学习表征的度量景观。研究显示，AugMix与弱增强在计算机视觉任务中表现出跨任务有效性，强调需针对特定领域定制噪声方案。我们的发现凸显了同一领域内噪声组合与超参数迁移的成功性，但跨领域迁移效益存在困难。此外，研究揭示了同时优化泛化与校准的复杂性，强调实践者需谨慎考虑噪声组合与超参数调优，以实现特定任务与数据集的最优性能。