Abstract Machine learning using quantum convolutional neural networks (QCNNs) has
demonstrated success in both quantum and classical data classification. In previous studies …

Deep learning technology has been extensively studied for its potential in music, notably for
creative music generation research. Traditional music generation approaches based on …

Recently, Moore-Penrose inverse (MPI)-based parameter fine-tuning of fully connected (FC)
layers in pretrained deep convolutional neural networks (DCNNs) has emerged within the …

Multi-exposure image fusion aims to generate a single high-dynamic image by integrating
images with different exposures. Existing deep learning-based multi-exposure image fusion …

High-frequency surface wave radar (HFSWR) is a powerful tool for ship detection and
surveillance. blackHowever, the use of pre-trained deep learning (DL) networks for ship …

In this letter, we propose a novel semi-supervised subspace clustering method, which is
able to simultaneously augment the initial supervisory information and construct a …

Filter pruning can effectively reduce the time cost and computing resources of convolutional
neural networks (CNNs), and is well applied to lightweight edge devices. However, most of …

The broad learning system (BLS) is a versatile and effective tool for analyzing tabular data.
However, the rapid expansion of big data has resulted in an overwhelming amount of …

The graph-information-based fuzzy clustering has shown promising results in various
datasets. However, its performance is hindered when dealing with high-dimensional data …

Filter pruning has emerged as a crucial technique facilitating the efficient deployment of
Convolutional Neural Networks (CNNs) on edge devices. The resultant lightweight models …