Core Concepts
Systematic exploration of diverse noise injection methods reveals that certain noise types, such as AugMix, weak augmentation, and Dropout, can effectively improve both the generalization and calibration of neural networks across various datasets, tasks, and architectures. The findings emphasize the need for tailored noise approaches for specific domains and careful hyperparameter tuning when combining multiple noises.
Abstract
The study investigates the impact of various noise injection methods on the generalization and calibration of neural networks (NNs) across diverse datasets, tasks, and architectures. The authors explore a wide range of noise types, including input, input-target, target, activation, weight, gradient, and model noises.
Key highlights:
AugMix, weak augmentation, and Dropout prove effective across computer vision (CV) tasks, emphasizing their versatility.
Task-specific nuances in noise effectiveness, such as AugMix's superiority in CV, Dropout in natural language processing (NLP), and Gaussian noise in tabular data regression, highlight the need for tailored approaches.
Combining noises and careful hyperparameter tuning are crucial for optimizing NN's performance, as the relationship between generalization and calibration is complex.
The study evaluates NN performance on both in-distribution (ID) and out-of-distribution (OOD) settings, revealing that the best ID noise types often remain the best OOD, but the correlations between ID and OOD rankings are not always high.
MixUp and CMixUp (for regression) show surprising behavior, as they are much more helpful for improving OOD calibration than ID calibration.
The authors provide a comprehensive and systematic analysis of noise injection methods, offering valuable insights for practitioners to enhance NN generalization and calibration in specific tasks and datasets.
Stats
"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."
"Our study shows that AugMix and weak augmentation exhibit cross-task effectiveness in computer vision, emphasising the need to tailor noise to specific domains."
"The 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."
Quotes
"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."
"Our study shows that AugMix and weak augmentation exhibit cross-task effectiveness in computer vision, emphasising the need to tailor noise to specific domains."
"The 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."