This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution
with a focus on the proposed solutions and results. The aim of this challenge is to devise a …

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

Lightweight deep convolutional neural networks (LDCNNs) are vital components of mobile
intelligence, particularly in mobile vision. Although various heavy networks with increasingly …

Large pre-trained models (LPMs), such as LLaMA and GLM, have shown exceptional
performance across various tasks through fine-tuning. Although low-rank adaption (LoRA) …

Recently, the scale of transformers has grown rapidly, which introduces considerable
challenges in terms of training overhead and inference efficiency in the scope of task …

In this paper, we propose a novel channel pruning method to reduce the computational and
storage costs of Convolutional Neural Networks (CNNs). Many existing one-shot pruning …

Most existing neural network pruning methods hand-crafted their importance criteria and
structures to prune. This constructs heavy and unintended dependencies on heuristics and …

Deep learning algorithms are increasingly employed at the edge. However, edge devices
are resource constrained and thus require efficient deployment of deep neural networks …

Neural image compression has surpassed state-of-the-art traditional codecs (H. 266/VVC)
for rate-distortion (RD) performance, but suffers from large complexity and separate models …

Advancements in artificial intelligence algorithms and models, along with embedded device
support, have resulted in the issue of high energy consumption and poor compatibility when …