3 accepted papers
Adversarial attack aims to generate deceptive inputs to fool a machine learning model. In deep learning, an adversarial input created for a specific neural network can also trick other neural networks. This intriguing property is known as black-box transferability of adversarial examples. To improve…
Data augmentation is an effective way to improve the generalization capability of modern deep learning models. However, the underlying augmentation methods mostly rely on handcrafted operations. Moreover, an augmentation policy useful to one dataset may not transfer well to other datasets. Therefore…
Data augmentation is an efficient way to expand a training dataset by creating additional artificial data. While data augmentation is found to be effective in improving the generalization capabilities of models for various machine learning tasks, the underlying augmentation methods are usually manua…