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PSAQ-ViT V2: Toward Accurate and General Data-Free Quantization for Vision Transformers 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 页码: 12
作者:  Li, Zhikai;  Chen, Mengjuan;  Xiao, Junrui;  Gu, Qingyi
收藏  |  浏览/下载：53/0  |  提交时间：2023/11/17
Data-free quantization  model compression  patch similarity  quantized vision transformers (ViTs)  
Theme-Aware Aesthetic Distribution Prediction With Full-Resolution Photographs 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 页码: 15
作者:  Jia, Gengyun;  Li, Peipei;  He, Ran
Adobe PDF(10845Kb)  |  收藏  |  浏览/下载：249/37  |  提交时间：2022/06/06
Aesthetic quality assessment (AQA)  full resolution  region of image (RoM) pooling  theme  
ECBC: Efficient Convolution via Blocked Columnizing 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 13
作者:  Zhao, Tianli;  Hu, Qinghao;  He, Xiangyu;  Xu, Weixiang;  Wang, Jiaxing;  Leng, Cong;  Cheng, Jian
Adobe PDF(3003Kb)  |  收藏  |  浏览/下载：292/32  |  提交时间：2022/01/27
Convolution  Tensors  Layout  Memory management  Indexes  Transforms  Performance evaluation  Convolutional neural networks (CNNs)  direct convolution  high performance computing for mobile devices  im2col convolution  memory-efficient convolution (MEC)  
Dynamical Channel Pruning by Conditional Accuracy Change for Deep Neural Networks 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 卷号: 无, 期号: 无, 页码: 无
作者:  Chen, Zhiqiang;  Xu, Ting-Bing;  Du, Changde;  Liu, Cheng-Lin;  He, Huiguang
浏览  |  Adobe PDF(4352Kb)  |  收藏  |  浏览/下载：282/64  |  提交时间：2021/01/27
Conditional accuracy change (CAC), direct criterion, dynamical channel pruning, neural network compression, structure shaping.  
Dynamic Collaborative Tracking 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 卷号: 30, 期号: 10, 页码: 3035-3046
作者:  Zhu, Guibo;  Zhang, Zhaoxiang;  Wang, Jinqiao;  Wu, Yi;  Lu, Hanqing
浏览  |  Adobe PDF(2492Kb)  |  收藏  |  浏览/下载：497/151  |  提交时间：2018/10/11
Correlation filter (CF)  distracter suppression  online learning  visual tracking