Normalization techniques are essential for accelerating the training and improving the
generalization of deep neural networks (DNNs), and have successfully been used in various …

Recently, human activity recognition (HAR) that uses wearable sensors has become a
research hotspot due to its wide applications in a large variety of real-world scenarios …

Traditional normalization techniques (eg, Batch Normalization and Instance Normalization)
generally and simplistically assume that training and test data follow the same distribution …

Batch normalization (BN) is a milestone technique in deep learning. It normalizes the
activation using mini-batch statistics during training but the estimated population statistics …

Batch normalization (BN) is an important technique commonly incorporated into deep
learning models to perform standardization within mini-batches. The merits of BN in …

Compared with traditional convolutions, grouped convolutional neural networks are
promising for both model performance and network parameters. However, existing models …

Abstract Convolutional Neural Networks (CNNs) are typically constructed by stacking
multiple building blocks, each of which contains a normalization layer such as batch …

Normalization techniques are crucial in stabilizing and accelerating the training of deep
neural networks. However, they are mainly designed for the independent and identically …

Deep neural networks (DNNs) have achieved remarkable success in various fields, and two
powerful techniques, feature normalization and attention mechanisms, have been widely …

Improving existing neural network architectures can involve several design choices such as
manipulating the loss functions, employing a diverse learning strategy, exploiting gradient …