To ensure good performance, modern machine learning models typically require large
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …

Neural networks are prone to overfitting and memorizing data patterns. To avoid over-fitting
and enhance their generalization and performance, various methods have been suggested …

Despite substantial progress in the field of deep learning, overfitting persists as a critical
challenge, and data augmentation has emerged as a particularly promising approach due to …

Contrastive learning has become a key component of self-supervised learning approaches
for computer vision. By learning to embed two augmented versions of the same image close …

Modern data augmentation using a mixture-based technique can regularize the models from
overfitting to the training data in various computer vision applications, but a proper data …

Mixup-based augmentation has been found to be effective for generalizing models during
training, especially for Vision Transformers (ViTs) since they can easily overfit. However …

Data augmentation (DA) is an essential technique for training state-of-the-art deep learning
systems. In this paper, we empirically show data augmentation might introduce noisy …

CutMix is a popular augmentation technique commonly used for training modern
convolutional and transformer vision networks. It was originally designed to encourage …

Deep learning has achieved promising results in pavement distress detection. However, the
training model's effectiveness varies according to the data and scenarios acquired by …

As 3D point cloud analysis has received increasing attention, the insufficient scale of point
cloud datasets and the weak generalization ability of networks become prominent. In this …