Abstract Knowledge distillation conditioned on intermediate feature representations always
leads to significant performance improvements. Conventional feature distillation framework …

Deep Neural Networks (DNNs) have shown significant advantages in a wide variety of
domains. However, DNNs are becoming computationally intensive and energy hungry at an …

Co-adaptation of units is one of the most critical concerns in deep neural networks (DNNs),
which leads to overfitting. Dropout has been an exciting research subject in recent years to …

Dropout regularization has been widely used in deep learning but performs less effective for
convolutional neural networks since the spatially correlated features allow dropped …

In recent years, convolutional neural networks (CNNs) have been used in many fields.
Nowadays, CNNs have a high learning capability, and this learning capability is …

This paper presents a novel study: remote-sensing image translation between high-
resolution optical and Synthetic Aperture Radar (SAR) data through machine learning …

Deep networks have been of considerable interest in literature and have enabled the
solution of recent real‐world applications. Due to filters that offer feature extraction …

The convolutional neural network is a very important model of deep learning. It can help
avoid the exploding/vanishing gradient problem and improve the generalizability of a neural …

This study proposed a novel TFNNS method, which aimed to solve the imbalanced
phonocardiogram (PCG) signals' classification. TFFNS consisted of three submodules …

Computational neuroscience studies have shown that the structure of neural variability to an
unchanged stimulus affects the amount of information encoded. Some artificial deep neural …