Abstract Deep Convolutional Neural Networks have made an incredible progress in many
Computer Vision tasks. This progress, however, often relies on the availability of large …

Convolutional Neural Networks (CNNs) are used in many domains but the requirement of
large datasets for robust training sessions and no overfitting makes them hard to apply in …

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 …

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 …

Deep convolutional neural networks have performed remarkably well on many Computer
Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting …

Data augmentation (DA) is indispensable in modern machine learning and deep neural
networks. The basic idea of DA is to construct new training data to improve the model's …

Deep convolutional neural networks have shown impressive results on different computer
vision tasks. Nowadays machines are fed by new artificial intelligence techniques which …

Deep learning (DL) algorithms have shown significant performance in various computer
vision tasks. However, having limited labelled data lead to a network overfitting problem …

Data augmentation is an effective way to improve the generalization of deep learning
models. However, the underlying augmentation methods mainly rely on handcrafted …

This paper presents a supervised mixing augmentation method termed SuperMix, which
exploits the salient regions within input images to construct mixed training samples …