Data augmentation is arguably the most important regularization technique commonly used
to improve generalization performance of machine learning models. It primarily involves the …

In recent years, deep learning has been applied to a variety of scenarios in Industrial
Internet of Things (IIoT), including enhancing the security of IIoT. However, the existing deep …

Automatic data augmentation is a technique to automatically search for strategies for image
transformations, which can improve the performance of different vision tasks. RandAugment …

Data augmentation has become an integral part of deep learning, as it is known to improve
the generalization capabilities of neural networks. Since the most effective set of image …

In this paper, we propose multi-agent automated machine learning (MA2ML) with the aim to
effectively handle joint optimization of modules in automated machine learning (AutoML) …

Data augmentation plays a crucial role in deep learning-based medical imaging analysis,
but manually designing tailored data augmentation strategies for each dataset is impractical …

Hyperparameter optimization (HPO) methods alleviate the significant effort required to
obtain hyperparameters that perform optimally on visual learning problems. Existing …

Camera localization is a fundamental problem for many applications in computer vision,
robotics, and autonomy. Despite recent deep learning-based approaches, the lack of …

Neural architecture search (NAS) has been an active direction of automatic machine
learning (Auto-ML), aiming to explore efficient network structures. The searched architecture …

Data augmentation plays a crucial role in deep learning-based medical imaging analysis,
but manually designing tailored data augmentation strategies for each dataset is impractical …