Search Results for author: Guangcan Liu

Found 28 papers, 7 papers with code

ChipSong: A Controllable Lyric Generation System for Chinese Popular Song

1 code implementation In2Writing (ACL) 2022 Nayu Liu, Wenjing Han, Guangcan Liu, Da Peng, Ran Zhang, Xiaorui Wang, Huabin Ruan

In this work, we take a further step towards satisfying practical demands in Chinese lyric generation from musical short-video creators, in respect of the challenges on songs’ format constraints, creating specific lyrics from open-ended inspiration inputs, and language rhyme grace.

Language Modelling

Optimization Induced Equilibrium Networks

no code implementations27 May 2021 Xingyu Xie, Qiuhao Wang, Zenan Ling, Xia Li, Yisen Wang, Guangcan Liu, Zhouchen Lin

In this paper, we investigate an emerging question: can an implicit equilibrium model's equilibrium point be regarded as the solution of an optimization problem?

Time Series Forecasting via Learning Convolutionally Low-Rank Models

1 code implementation23 Apr 2021 Guangcan Liu

Recently, Liu and Zhang studied the rather challenging problem of time series forecasting from the perspective of compressed sensing.

Time Series Time Series Forecasting

Fast and Differentiable Matrix Inverse and Its Extension to SVD

no code implementations1 Jan 2021 Xingyu Xie, Hao Kong, Jianlong Wu, Guangcan Liu, Zhouchen Lin

First of all, to perform matrix inverse, we provide a differentiable yet efficient way, named LD-Minv, which is a learnable deep neural network (DNN) with each layer being an $L$-th order matrix polynomial.

Maximum-and-Concatenation Networks

1 code implementation ICML 2020 Xingyu Xie, Hao Kong, Jianlong Wu, Wayne Zhang, Guangcan Liu, Zhouchen Lin

While successful in many fields, deep neural networks (DNNs) still suffer from some open problems such as bad local minima and unsatisfactory generalization performance.

Learning Hybrid Representation by Robust Dictionary Learning in Factorized Compressed Space

no code implementations26 Dec 2019 Jiahuan Ren, Zhao Zhang, Sheng Li, Yang Wang, Guangcan Liu, Shuicheng Yan, Meng Wang

Specifically, J-RFDL performs the robust representation by DL in a factorized compressed space to eliminate the negative effects of noise and outliers on the results, which can also make the DL process efficient.

Dictionary Learning

Multilayer Collaborative Low-Rank Coding Network for Robust Deep Subspace Discovery

no code implementations13 Dec 2019 Xianzhen Li, Zhao Zhang, Yang Wang, Guangcan Liu, Shuicheng Yan, Meng Wang

In this paper, we explore the deep multi-subspace recovery problem by designing a multilayer architecture for latent LRR.

Representation Learning

Recovery of Future Data via Convolution Nuclear Norm Minimization

1 code implementation6 Sep 2019 Guangcan Liu, Wayne Zhang

This paper studies the problem of time series forecasting (TSF) from the perspective of compressed sensing.

Time Series Time Series Forecasting

Flexible Auto-weighted Local-coordinate Concept Factorization: A Robust Framework for Unsupervised Clustering

no code implementations2 Sep 2019 Zhao Zhang, Yan Zhang, Sheng Li, Guangcan Liu, Dan Zeng, Shuicheng Yan, Meng Wang

For auto-weighting, RFA-LCF jointly preserves the manifold structures in the basis concept space and new coordinate space in an adaptive manner by minimizing the reconstruction errors on clean data, anchor points and coordinates.

Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification

no code implementations21 Aug 2019 Zhao Zhang, Yulin Sun, Zheng Zhang, Yang Wang, Guangcan Liu, Meng Wang

In this setting, our TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner.

General Classification

Kernel-Induced Label Propagation by Mapping for Semi-Supervised Classification

no code implementations29 May 2019 Zhao Zhang, Lei Jia, Mingbo Zhao, Guangcan Liu, Meng Wang, Shuicheng Yan

A Kernel-Induced Label Propagation (Kernel-LP) framework by mapping is proposed for high-dimensional data classification using the most informative patterns of data in kernel space.

General Classification

Robust Unsupervised Flexible Auto-weighted Local-Coordinate Concept Factorization for Image Clustering

no code implementations25 May 2019 Zhao Zhang, Yan Zhang, Sheng Li, Guangcan Liu, Meng Wang, Shuicheng Yan

RFA-LCF integrates the robust flexible CF, robust sparse local-coordinate coding and the adaptive reconstruction weighting learning into a unified model.

Image Clustering Representation Learning

Joint Label Prediction based Semi-Supervised Adaptive Concept Factorization for Robust Data Representation

no code implementations25 May 2019 Zhao Zhang, Yan Zhang, Guangcan Liu, Jinhui Tang, Shuicheng Yan, Meng Wang

To enrich prior knowledge to enhance the discrimination, RS2ACF clearly uses class information of labeled data and more importantly propagates it to unlabeled data by jointly learning an explicit label indicator for unlabeled data.

Scalable Block-Diagonal Locality-Constrained Projective Dictionary Learning

no code implementations25 May 2019 Zhao Zhang, Weiming Jiang, Zheng Zhang, Sheng Li, Guangcan Liu, Jie Qin

More importantly, LC-PDL avoids using the complementary data matrix to learn the sub-dictionary over each class.

Dictionary Learning

Differentiable Linearized ADMM

1 code implementation15 May 2019 Xingyu Xie, Jianlong Wu, Zhisheng Zhong, Guangcan Liu, Zhouchen Lin

Recently, a number of learning-based optimization methods that combine data-driven architectures with the classical optimization algorithms have been proposed and explored, showing superior empirical performance in solving various ill-posed inverse problems, but there is still a scarcity of rigorous analysis about the convergence behaviors of learning-based optimization.

Matrix Recovery with Implicitly Low-Rank Data

1 code implementation9 Nov 2018 Xingyu Xie, Jianlong Wu, Guangcan Liu, Jun Wang

To tackle this issue, we propose a novel method for matrix recovery in this paper, which could well handle the case where the target matrix is low-rank in an implicit feature space but high-rank or even full-rank in its original form.

Robust Subspace Clustering with Compressed Data

no code implementations30 Mar 2018 Guangcan Liu, Zhao Zhang, Qingshan Liu, Kongkai Xiong

Dimension reduction is widely regarded as an effective way for decreasing the computation, storage and communication loads of data-driven intelligent systems, leading to a growing demand for statistical methods that allow analysis (e. g., clustering) of compressed data.

Dimensionality Reduction

A New Theory for Matrix Completion

no code implementations NeurIPS 2017 Guangcan Liu, Qingshan Liu, Xiaotong Yuan

To break through the limits of random sampling, this paper introduces a new hypothesis called \emph{isomeric condition}, which is provably weaker than the assumption of uniform sampling and arguably holds even when the missing data is placed irregularly.

Matrix Completion

Detection of Moving Object in Dynamic Background Using Gaussian Max-Pooling and Segmentation Constrained RPCA

no code implementations3 Sep 2017 Yang Li, Guangcan Liu, Sheng-Yong Chen

Due to its efficiency and stability, Robust Principal Component Analysis (RPCA) has been emerging as a promising tool for moving object detection.

Moving Object Detection

Learning Additive Exponential Family Graphical Models via \ell_{2,1}-norm Regularized M-Estimation

no code implementations NeurIPS 2016 Xiaotong Yuan, Ping Li, Tong Zhang, Qingshan Liu, Guangcan Liu

We investigate a subclass of exponential family graphical models of which the sufficient statistics are defined by arbitrary additive forms.

Low-Rank Tensor Constrained Multiview Subspace Clustering

no code implementations ICCV 2015 Changqing Zhang, Huazhu Fu, Si Liu, Guangcan Liu, Xiaochun Cao

We introduce a low-rank tensor constraint to explore the complementary information from multiple views and, accordingly, establish a novel method called Low-rank Tensor constrained Multiview Subspace Clustering (LT-MSC).

A Latent Clothing Attribute Approach for Human Pose Estimation

no code implementations16 Nov 2014 Weipeng Zhang, Jie Shen, Guangcan Liu, Yong Yu

Unlike previous approaches, our approach models the clothing attributes as latent variables and thus requires no explicit labeling for the clothing attributes.

Action Recognition Pose Estimation

Unified Structured Learning for Simultaneous Human Pose Estimation and Garment Attribute Classification

no code implementations19 Apr 2014 Jie Shen, Guangcan Liu, Jia Chen, Yuqiang Fang, Jianbin Xie, Yong Yu, Shuicheng Yan

In this paper, we utilize structured learning to simultaneously address two intertwined problems: human pose estimation (HPE) and garment attribute classification (GAC), which are valuable for a variety of computer vision and multimedia applications.

General Classification Pose Estimation

Advancing Matrix Completion by Modeling Extra Structures beyond Low-Rankness

no code implementations17 Apr 2014 Guangcan Liu, Ping Li

To better handle non-uniform data, in this paper we propose a method termed Low-Rank Factor Decomposition (LRFD), which imposes an additional restriction that the data points must be represented as linear combinations of the bases in a dictionary constructed or learnt in advance.

Low-Rank Matrix Completion

Recovery of Coherent Data via Low-Rank Dictionary Pursuit

no code implementations NeurIPS 2014 Guangcan Liu, Ping Li

More precisely, we mathematically prove that if the dictionary itself is low-rank then LRR is immune to the coherence parameter which increases with the underlying cluster number.

Blind Image Deblurring by Spectral Properties of Convolution Operators

no code implementations10 Sep 2012 Guangcan Liu, Shiyu Chang, Yi Ma

We show that the minimizer of this regularizer guarantees to give good approximation to the blur kernel if the original image is sharp enough.

Blind Image Deblurring Image Deblurring

Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation

no code implementations8 Sep 2011 Guangcan Liu, Huan Xu, Shuicheng Yan

In this work, we address the following matrix recovery problem: suppose we are given a set of data points containing two parts, one part consists of samples drawn from a union of multiple subspaces and the other part consists of outliers.

Outlier Detection

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