147 citations · 334 across the 7 of their papers we have counts for
7 papers
Neural Network Pruning with Residual-Connections and Limited-Data
Jian-Hao Luo, Jianxin Wu
Filter level pruning is an effective method to accelerate the inference speed of deep CNN models. Although numerous pruning algorithms have been proposed, there are still two open…
AutoPruner: An End-to-End Trainable Filter Pruning Method for Efficient Deep Model Inference
Jian-Hao Luo, Jianxin Wu
Channel pruning is an important family of methods to speed up deep model's inference. Previous filter pruning algorithms regard channel pruning and model fine-tuning as two indepen…
Learning Effective Binary Visual Representations with Deep Networks
Jianxin Wu, Jian-Hao Luo
Although traditionally binary visual representations are mainly designed to reduce computational and storage costs in the image retrieval research, this paper argues that binary vi…
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression
Jian-Hao Luo, Jianxin Wu, Weiyao Lin
We propose an efficient and unified framework, namely ThiNet, to simultaneously accelerate and compress CNN models in both training and inference stages. We focus on the filter lev…
An Entropy-based Pruning Method for CNN Compression
Jian-Hao Luo, Jianxin Wu
This paper aims to simultaneously accelerate and compress off-the-shelf CNN models via filter pruning strategy. The importance of each filter is evaluated by the proposed entropy-b…
Dense CNN Learning with Equivalent Mappings
Jianxin Wu, Chen-Wei Xie, Jian-Hao Luo
Large receptive field and dense prediction are both important for achieving high accuracy in pixel labeling tasks such as semantic segmentation. These two properties, however, cont…
Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval
Xiu-Shen Wei, Jian-Hao Luo, Jianxin Wu +1
Deep convolutional neural network models pre-trained for the ImageNet classification task have been successfully adopted to tasks in other domains, such as texture description and…