14 citations · 14 across the 4 of their papers we have counts for
4 papers
Expandable, Compressible, Mineable: Open-World Thermal Image Restoration
Pu Li, Huafeng Li, Yafei Zhang +3
In open-world settings, thermal infrared (TIR) image degradations continuously emerge and evolve, while most existing all-in-one restoration methods are built on a closed-set assum…
RAPQ: Rescuing Accuracy for Power-of-Two Low-bit Post-training Quantization
Hongyi Yao, Pu Li, Jian Cao +3
We introduce a Power-of-Two low-bit post-training quantization(PTQ) method for deep neural network that meets hardware requirements and does not call for long-time retraining. Powe…
An Once-for-All Budgeted Pruning Framework for ConvNets Considering Input Resolution
Wenyu Sun, Jian Cao, Pengtao Xu +2
We propose an efficient once-for-all budgeted pruning framework (OFARPruning) to find many compact network structures close to winner tickets in the early training stage considerin…
Layer Pruning via Fusible Residual Convolutional Block for Deep Neural Networks
Pengtao Xu, Jian Cao, Fanhua Shang +2
In order to deploy deep convolutional neural networks (CNNs) on resource-limited devices, many model pruning methods for filters and weights have been developed, while only a few t…