Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …

Over the past decade, deep-learning-based representations have demonstrated remarkable
performance in academia and industry. The learning capability of convolutional neural …

Structural pruning enables model acceleration by removing structurally-grouped parameters
from neural networks. However, the parameter-grou** patterns vary widely across …

The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

Channel (or 3D filter) pruning serves as an effective way to accelerate the inference of
neural networks. There has been a flurry of algorithms that try to solve this practical problem …

Convolutional neural network (CNN) pruning has become one of the most successful
network compression approaches in recent years. Existing works on network pruning …

Filter pruning has been widely used for neural network compression because of its enabled
practical acceleration. To date, most of the existing filter pruning works explore the …

With the rapid development of facial manipulation techniques, face forgery has received
considerable attention in digital media forensics due to security concerns. Most existing …

Network compression has been widely studied since it is able to reduce the memory and
computation cost during inference. However, previous methods seldom deal with …

Finding out the computational redundant part of a trained Deep Neural Network (DNN) is the
key question that pruning algorithms target on. Many algorithms try to predict model …