Search Results for author: Jian-Hao Luo

Found 7 papers, 1 papers with code

AutoPruner: An End-to-End Trainable Filter Pruning Method for Efficient Deep Model Inference

no code implementations23 May 2018 Jian-Hao Luo, Jianxin Wu

Previous filter pruning algorithms regard channel pruning and model fine-tuning as two independent steps.

Binarization Fine-tuning

Learning Effective Binary Visual Representations with Deep Networks

no code implementations8 Mar 2018 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 visual representations can be applied to large scale recognition and detection problems in addition to hashing in retrieval.

General Classification Image Retrieval +1

ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression

no code implementations ICCV 2017 Jian-Hao Luo, Jianxin Wu, Weiyao Lin

Similar experiments with ResNet-50 reveal that even for a compact network, ThiNet can also reduce more than half of the parameters and FLOPs, at the cost of roughly 1$\%$ top-5 accuracy drop.

Neural Network Compression

An Entropy-based Pruning Method for CNN Compression

no code implementations19 Jun 2017 Jian-Hao Luo, Jianxin Wu

Experiments on the ILSVRC-12 benchmark demonstrate the effectiveness of our method.


Dense CNN Learning with Equivalent Mappings

no code implementations24 May 2016 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.

Age Estimation Image Categorization +2

Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval

no code implementations18 Apr 2016 Xiu-Shen Wei, Jian-Hao Luo, Jianxin Wu, Zhi-Hua Zhou

Moreover, on general image retrieval datasets, SCDA achieves comparable retrieval results with state-of-the-art general image retrieval approaches.

Image Retrieval Object Proposal Generation

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