Search Results for author: Pu Li

Found 7 papers, 1 papers with code

RAPQ: Rescuing Accuracy for Power-of-Two Low-bit Post-training Quantization

1 code implementation26 Apr 2022 Hongyi Yao, Pu Li, Jian Cao, Xiangcheng Liu, Chenying Xie, Bingzhang Wang

We are the first to propose the more constrained but hardware-friendly Power-of-Two quantization scheme for low-bit PTQ specially and prove that it can achieve nearly the same accuracy as SOTA PTQ method.

Quantization

MVP-Net: Multiple View Pointwise Semantic Segmentation of Large-Scale Point Clouds

no code implementations30 Jan 2022 Chuanyu Luo, Xiaohan Li, Nuo Cheng, Han Li, Shengguang Lei, Pu Li

The pipeline of most pointwise point cloud semantic segmentation methods includes points sampling, neighbor searching, feature aggregation, and classification.

Autonomous Driving Semantic Segmentation

An Once-for-All Budgeted Pruning Framework for ConvNets Considering Input Resolution

no code implementations2 Dec 2020 Wenyu Sun, Jian Cao, Pengtao Xu, Xiangcheng Liu, Pu Li

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 considering the effect of input resolution during the pruning process.

Image Classification object-detection +1

Layer Pruning via Fusible Residual Convolutional Block for Deep Neural Networks

no code implementations29 Nov 2020 Pengtao Xu, Jian Cao, Fanhua Shang, Wenyu Sun, Pu Li

For layer pruning, we convert convolutional layers of network into ResConv with a layer scaling factor.

FenceMask: A Data Augmentation Approach for Pre-extracted Image Features

no code implementations14 Jun 2020 Pu Li, Xiang-Yang Li, Xiang Long

It is based on the 'simulation of object occlusion' strategy, which aim to achieve the balance between object occlusion and information retention of the input data.

Data Augmentation Fine-Grained Visual Categorization

Review of Text Style Transfer Based on Deep Learning

no code implementations6 May 2020 Xiang-Yang Li, Guo Pu, Keyu Ming, Pu Li, Jie Wang, Yuxuan Wang

In the traditional text style transfer model, the text style is generally relied on by experts knowledge and hand-designed rules, but with the application of deep learning in the field of natural language processing, the text style transfer method based on deep learning Started to be heavily researched.

Style Transfer Text Style Transfer

Fabry-Perot Lasers as Enablers for Parallel Reservoir Computing

no code implementations4 May 2020 Adonis Bogris, Charis Mesaritakis, Stavros Deligiannidis, Pu Li

We introduce the use of Fabry-Perot (FP) lasers as potential neuromorphic computing machines with parallel processing capabilities.

Classification General Classification

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