Search Results for author: Shupeng Gui

Found 11 papers, 4 papers with code

GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework

2 code implementations ECCV 2020 Haotao Wang, Shupeng Gui, Haichuan Yang, Ji Liu, Zhangyang Wang

Generative adversarial networks (GANs) have gained increasing popularity in various computer vision applications, and recently start to be deployed to resource-constrained mobile devices.

Image-to-Image Translation Model distillation +2

Hierarchical Prototype Learning for Zero-Shot Recognition

no code implementations24 Oct 2019 Xingxing Zhang, Shupeng Gui, Zhenfeng Zhu, Yao Zhao, Ji Liu

Specifically, HPL is able to obtain discriminability on both seen and unseen class domains by learning visual prototypes respectively under the transductive setting.

Image Captioning Object Recognition +1

ATZSL: Defensive Zero-Shot Recognition in the Presence of Adversaries

no code implementations24 Oct 2019 Xingxing Zhang, Shupeng Gui, Zhenfeng Zhu, Yao Zhao, Ji Liu

In this paper, we take an initial attempt, and propose a generic formulation to provide a systematical solution (named ATZSL) for learning a robust ZSL model.

Image Captioning Object Recognition +1

Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-based Approach

1 code implementation CVPR 2020 Haichuan Yang, Shupeng Gui, Yuhao Zhu, Ji Liu

A key parameter that all existing compression techniques are sensitive to is the compression ratio (e. g., pruning sparsity, quantization bitwidth) of each layer.

Neural Network Compression Quantization

Model Compression with Adversarial Robustness: A Unified Optimization Framework

1 code implementation NeurIPS 2019 Shupeng Gui, Haotao Wang, Chen Yu, Haichuan Yang, Zhangyang Wang, Ji Liu

Deep model compression has been extensively studied, and state-of-the-art methods can now achieve high compression ratios with minimal accuracy loss.

Adversarial Robustness Model Compression +1

P^2IR: Universal Deep Node Representation via Partial Permutation Invariant Set Functions

no code implementations27 Sep 2018 Shupeng Gui, Xiangliang Zhang, Shuang Qiu, Mingrui Wu, Jieping Ye, Ji Liu

Our method can 1) learn an arbitrary form of the representation function from the neighborhood, without losing any potential dependence structures, 2) automatically decide the significance of neighbors at different distances, and 3) be applicable to both homogeneous and heterogeneous graph embedding, which may contain multiple types of nodes.

Graph Embedding

GESF: A Universal Discriminative Mapping Mechanism for Graph Representation Learning

no code implementations28 May 2018 Shupeng Gui, Xiangliang Zhang, Shuang Qiu, Mingrui Wu, Jieping Ye, Ji Liu

Graph embedding is a central problem in social network analysis and many other applications, aiming to learn the vector representation for each node.

Graph Embedding Graph Representation Learning

On The Projection Operator to A Three-view Cardinality Constrained Set

no code implementations ICML 2017 Haichuan Yang, Shupeng Gui, Chuyang Ke, Daniel Stefankovic, Ryohei Fujimaki, Ji Liu

The cardinality constraint is an intrinsic way to restrict the solution structure in many domains, for example, sparse learning, feature selection, and compressed sensing.

Feature Selection Sparse Learning

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