Vision-language pre-trained models have achieved impressive performance on various
downstream tasks. However their large model sizes hinder their utilization on platforms with …

Neurologically, filter pruning is a procedure of forgetting and remembering recovering.
Prevailing methods directly forget less important information from an unrobust baseline at …

Convolutional neural networks (CNNs) have been successfully employed in remote sensing
image classification because of their robust feature representation for different visual tasks …

Interpretability has recently gained attention in the field of machine learning, for it is crucial
when it comes to high-stakes decisions or troubleshooting. This abstract concept is hard to …

Predicting the binding sites of target proteins plays a fundamental role in drug discovery.
Most existing deep-learning methods consider a protein as a 3D image by spatially …

Filter pruning simultaneously accelerates the computation and reduces the memory
overhead of CNNs, which can be effectively applied to edge devices and cloud services. In …

Existing deep-learning-based image steganalysis networks have problems such as large
model sizes, significant runtime memory usage, and extensive computational operations …

Handling the ever-increasing scale of contemporary deep learning and transformer-based
models poses a significant challenge. Overparameterized Transformer networks outperform …

In deep learning, the size and complexity of neural networks have rapidly increased to
achieve higher performance. However, this poses a challenge when utilized in resource …

With the increase in model parameters, deep neural networks (DNNs) have achieved
remarkable performance in computer vision, but larger DNNs create a bottleneck for …