In machine learning, the model is not as complicated as possible. Good generalization
ability means that the model not only performs well on the training data set, but also can …

Advances in computing technology have fostered the development of new and powerful
deep learning (DL) techniques, which have demonstrated promising results in a wide range …

Dropout is a powerful and widely used technique to regularize the training of deep neural
networks. Though effective and performing well, the randomness introduced by dropout …

Domain-specific hardware is becoming a promising topic in the backdrop of improvement
slow down for general-purpose processors due to the foreseeable end of Moore's Law …

Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying
machine learning models in the real world. Previous methods commonly rely on an OOD …

The advent of deep learning has brought great change to the community of computer
science and also revitalized numerous fields where traditional machine learning methods …

We propose a novel data augmentation methodGridMask'in this paper. It utilizes information
removal to achieve state-of-the-art results in a variety of computer vision tasks. We analyze …

Introduced by Hinton et al. in 2012, dropout has stood the test of time as a regularizer for
preventing overfitting in neural networks. In this study, we demonstrate that dropout can also …

Recently, social touch gesture recognition has been considered an important topic for touch
modality, which can lead to highly efficient and realistic human‐robot interaction. In this …

In this paper, we introduce Random Erasing, a new data augmentation method for training
the convolutional neural network (CNN). In training, Random Erasing randomly selects a …