Search Results for author: Chao Xu

Found 76 papers, 27 papers with code

A Cognitively Motivated Approach to Spatial Information Extraction

no code implementations EMNLP (SpLU) 2020 Chao Xu, Emmanuelle-Anna Dietz Saldanha, Dagmar Gromann, Beihai Zhou

We propose an automated spatial semantic analysis (ASSA) framework building on grammar and cognitive linguistic theories to identify spatial entities and relations, bringing together methods of spatial information extraction and cognitive frameworks on spatial language.

Source-Free Domain Adaptation via Distribution Estimation

no code implementations24 Apr 2022 Ning Ding, Yixing Xu, Yehui Tang, Chao Xu, Yunhe Wang, DaCheng Tao

Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different.

Domain Adaptation

Towards Homogeneous Modality Learning and Multi-Granularity Information Exploration for Visible-Infrared Person Re-Identification

no code implementations11 Apr 2022 Haojie Liu, Daoxun Xia, Wei Jiang, Chao Xu

In order to mitigate the impact of large modality discrepancy existing in heterogeneous images, previous methods attempt to apply generative adversarial network (GAN) to generate the modality-consisitent data.

Person Re-Identification

Region-Aware Face Swapping

no code implementations9 Mar 2022 Chao Xu, Jiangning Zhang, Miao Hua, Qian He, Zili Yi, Yong liu

This paper presents a novel Region-Aware Face Swapping (RAFSwap) network to achieve identity-consistent harmonious high-resolution face generation in a local-global manner: \textbf{1)} Local Facial Region-Aware (FRA) branch augments local identity-relevant features by introducing the Transformer to effectively model misaligned cross-scale semantic interaction.

Face Generation Face Swapping

PartAfford: Part-level Affordance Discovery from 3D Objects

no code implementations28 Feb 2022 Chao Xu, Yixin Chen, He Wang, Song-Chun Zhu, Yixin Zhu, Siyuan Huang

We propose a novel learning framework for PartAfford, which discovers part-level representations by leveraging only the affordance set supervision and geometric primitive regularization, without dense supervision.

SCSNet: An Efficient Paradigm for Learning Simultaneously Image Colorization and Super-Resolution

no code implementations12 Jan 2022 Jiangning Zhang, Chao Xu, Jian Li, Yue Han, Yabiao Wang, Ying Tai, Yong liu

In the practical application of restoring low-resolution gray-scale images, we generally need to run three separate processes of image colorization, super-resolution, and dows-sampling operation for the target device.

Colorization Super-Resolution

Learning to dehaze with polarization

no code implementations NeurIPS 2021 Chu Zhou, Minggui Teng, Yufei Han, Chao Xu, Boxin Shi

Haze, a common kind of bad weather caused by atmospheric scattering, decreases the visibility of scenes and degenerates the performance of computer vision algorithms.

Image Dehazing Single Image Dehazing

An Image Patch is a Wave: Phase-Aware Vision MLP

4 code implementations24 Nov 2021 Yehui Tang, Kai Han, Jianyuan Guo, Chang Xu, Yanxi Li, Chao Xu, Yunhe Wang

To dynamically aggregate tokens, we propose to represent each token as a wave function with two parts, amplitude and phase.

Image Classification Object Detection +1

Positive and Unlabeled Federated Learning

no code implementations29 Sep 2021 Lin Xinyang, Hanting Chen, Yixing Xu, Chao Xu, Xiaolin Gui, Yiping Deng, Yunhe Wang

We study the problem of learning from positive and unlabeled (PU) data in the federated setting, where each client only labels a little part of their dataset due to the limitation of resources and time.

Federated Learning

TotalRecall: A Bidirectional Candidates Generation Framework for Large Scale Recommender \& Advertising Systems

no code implementations29 Sep 2021 Qifang Zhao, Yu Jiang, Yuqing Liu, Meng Du, Qinghui Sun, Chao Xu, Huan Xu, Zhongyao Wang

Recommender (RS) and Advertising/Marketing Systems (AS) play the key roles in E-commerce companies like Amazaon and Alibaba.

Hire-MLP: Vision MLP via Hierarchical Rearrangement

3 code implementations30 Aug 2021 Jianyuan Guo, Yehui Tang, Kai Han, Xinghao Chen, Han Wu, Chao Xu, Chang Xu, Yunhe Wang

Previous vision MLPs such as MLP-Mixer and ResMLP accept linearly flattened image patches as input, making them inflexible for different input sizes and hard to capture spatial information.

Image Classification Object Detection +1

Augmented Shortcuts for Vision Transformers

3 code implementations NeurIPS 2021 Yehui Tang, Kai Han, Chang Xu, An Xiao, Yiping Deng, Chao Xu, Yunhe Wang

Transformer models have achieved great progress on computer vision tasks recently.

Federated Learning with Positive and Unlabeled Data

no code implementations21 Jun 2021 Xinyang Lin, Hanting Chen, Yixing Xu, Chao Xu, Xiaolin Gui, Yiping Deng, Yunhe Wang

We study the problem of learning from positive and unlabeled (PU) data in the federated setting, where each client only labels a little part of their dataset due to the limitation of resources and time.

Federated Learning

Learning Student Networks in the Wild

1 code implementation CVPR 2021 Hanting Chen, Tianyu Guo, Chang Xu, Wenshuo Li, Chunjing Xu, Chao Xu, Yunhe Wang

Experiments on various datasets demonstrate that the student networks learned by the proposed method can achieve comparable performance with those using the original dataset.

Knowledge Distillation Model Compression

Patch Slimming for Efficient Vision Transformers

no code implementations5 Jun 2021 Yehui Tang, Kai Han, Yunhe Wang, Chang Xu, Jianyuan Guo, Chao Xu, DaCheng Tao

We first identify the effective patches in the last layer and then use them to guide the patch selection process of previous layers.

Analogous to Evolutionary Algorithm: Designing a Unified Sequence Model

1 code implementation NeurIPS 2021 Jiangning Zhang, Chao Xu, Jian Li, Wenzhou Chen, Yabiao Wang, Ying Tai, Shuo Chen, Chengjie Wang, Feiyue Huang, Yong liu

Inspired by biological evolution, we explain the rationality of Vision Transformer by analogy with the proven practical Evolutionary Algorithm (EA) and derive that both of them have consistent mathematical representation.

Image Retrieval

Universal Adder Neural Networks

no code implementations29 May 2021 Hanting Chen, Yunhe Wang, Chang Xu, Chao Xu, Chunjing Xu, Tong Zhang

The widely-used convolutions in deep neural networks are exactly cross-correlation to measure the similarity between input feature and convolution filters, which involves massive multiplications between float values.

Optimizing the Long-Term Average Reward for Continuing MDPs: A Technical Report

no code implementations13 Apr 2021 Chao Xu, Yiping Xie, Xijun Wang, Howard H. Yang, Dusit Niyato, Tony Q. S. Quek

cost), by integrating R-learning, a tabular reinforcement learning (RL) algorithm tailored for maximizing the long-term average reward, and traditional DRL algorithms, initially developed to optimize the discounted long-term cumulative reward rather than the average one.

reinforcement-learning

Visibility-aware Trajectory Optimization with Application to Aerial Tracking

1 code implementation11 Mar 2021 Qianhao Wang, Yuman Gao, Jialin Ji, Chao Xu, Fei Gao

The visibility of targets determines performance and even success rate of various applications, such as active slam, exploration, and target tracking.

Trajectory Planning Robotics

Manifold Regularized Dynamic Network Pruning

2 code implementations CVPR 2021 Yehui Tang, Yunhe Wang, Yixing Xu, Yiping Deng, Chao Xu, DaCheng Tao, Chang Xu

Then, the manifold relationship between instances and the pruned sub-networks will be aligned in the training procedure.

Network Pruning

Integrating Fast Regional Optimization into Sampling-based Kinodynamic Planning for Multirotor Flight

1 code implementation9 Mar 2021 Hongkai Ye, Tianyu Liu, Chao Xu, Fei Gao

For real-time multirotor kinodynamic motion planning, the efficiency of sampling-based methods is usually hindered by difficult-to-sample homotopy classes like narrow passages.

Motion Planning Robotics

EvIntSR-Net: Event Guided Multiple Latent Frames Reconstruction and Super-Resolution

no code implementations ICCV 2021 Jin Han, Yixin Yang, Chu Zhou, Chao Xu, Boxin Shi

To reconstruct high-resolution intensity images from event data, we propose EvIntSR-Net that converts event data to multiple latent intensity frames to achieve super-resolution on intensity images in this paper.

Frame Super-Resolution

Pre-Trained Image Processing Transformer

3 code implementations CVPR 2021 Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, Wen Gao

To maximally excavate the capability of transformer, we present to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs.

 Ranked #1 on Single Image Deraining on Rain100L (using extra training data)

Color Image Denoising Contrastive Learning +2

UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging

no code implementations NeurIPS 2020 Chu Zhou, Hang Zhao, Jin Han, Chang Xu, Chao Xu, Tiejun Huang, Boxin Shi

A conventional camera often suffers from over- or under-exposure when recording a real-world scene with a very high dynamic range (HDR).

HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation

1 code implementation CVPR 2021 Jiefeng Li, Chao Xu, Zhicun Chen, Siyuan Bian, Lixin Yang, Cewu Lu

We show that HybrIK preserves both the accuracy of 3D pose and the realistic body structure of the parametric human model, leading to a pixel-aligned 3D body mesh and a more accurate 3D pose than the pure 3D keypoint estimation methods.

3D human pose and shape estimation

Learning-based 3D Occupancy Prediction for Autonomous Navigation in Occluded Environments

1 code implementation8 Nov 2020 Lizi Wang, Hongkai Ye, Qianhao Wang, Yuman Gao, Chao Xu, Fei Gao

In autonomous navigation of mobile robots, sensors suffer from massive occlusion in cluttered environments, leaving significant amount of space unknown during planning.

Autonomous Navigation

APB2FaceV2: Real-Time Audio-Guided Multi-Face Reenactment

1 code implementation25 Oct 2020 Jiangning Zhang, Xianfang Zeng, Chao Xu, Jun Chen, Yong liu, Yunliang Jiang

Audio-guided face reenactment aims to generate a photorealistic face that has matched facial expression with the input audio.

Face Reenactment

SCOP: Scientific Control for Reliable Neural Network Pruning

4 code implementations NeurIPS 2020 Yehui Tang, Yunhe Wang, Yixing Xu, DaCheng Tao, Chunjing Xu, Chao Xu, Chang Xu

To increase the reliability of the results, we prefer to have a more rigorous research design by including a scientific control group as an essential part to minimize the effect of all factors except the association between the filter and expected network output.

Network Pruning

Deep Learning Based Antenna Selection for Channel Extrapolation in FDD Massive MIMO

no code implementations3 Sep 2020 Yindi Yang, Shun Zhang, Feifei Gao, Chao Xu, Jianpeng Ma, Octavia A. Dobre

In massive multiple-input multiple-output (MIMO) systems, the large number of antennas would bring a great challenge for the acquisition of the accurate channel state information, especially in the frequency division duplex mode.

EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors

1 code implementation20 Aug 2020 Xin Zhou, Zhepei Wang, Chao Xu, Fei Gao

Gradient-based planners are widely used for quadrotor local planning, in which a Euclidean Signed Distance Field (ESDF) is crucial for evaluating gradient magnitude and direction.

Robotics

CMPCC: Corridor-based Model Predictive Contouring Control for Aggressive Drone Flight

1 code implementation7 Jul 2020 Jialin Ji, Xin Zhou, Chao Xu, Fei Gao

In this paper, we propose an efficient, receding horizon, local adaptive low-level planner as the middle layer between our original planner and controller.

Robotics

HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens

1 code implementation CVPR 2021 Zhaohui Yang, Yunhe Wang, Xinghao Chen, Jianyuan Guo, Wei zhang, Chao Xu, Chunjing Xu, DaCheng Tao, Chang Xu

To achieve an extremely fast NAS while preserving the high accuracy, we propose to identify the vital blocks and make them the priority in the architecture search.

Neural Architecture Search

Hierarchical and Efficient Learning for Person Re-Identification

no code implementations18 May 2020 Jiangning Zhang, Liang Liu, Chao Xu, Yong liu

Recent works in the person re-identification task mainly focus on the model accuracy while ignore factors related to the efficiency, e. g. model size and latency, which are critical for practical application.

Person Re-Identification

A Semi-Supervised Assessor of Neural Architectures

no code implementations CVPR 2020 Yehui Tang, Yunhe Wang, Yixing Xu, Hanting Chen, Chunjing Xu, Boxin Shi, Chao Xu, Qi Tian, Chang Xu

A graph convolutional neural network is introduced to predict the performance of architectures based on the learned representations and their relation modeled by the graph.

Neural Architecture Search

Distilling portable Generative Adversarial Networks for Image Translation

no code implementations7 Mar 2020 Hanting Chen, Yunhe Wang, Han Shu, Changyuan Wen, Chunjing Xu, Boxin Shi, Chao Xu, Chang Xu

To promote the capability of student generator, we include a student discriminator to measure the distances between real images, and images generated by student and teacher generators.

Image-to-Image Translation Knowledge Distillation +1

Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks

2 code implementations23 Feb 2020 Yehui Tang, Yunhe Wang, Yixing Xu, Boxin Shi, Chao Xu, Chunjing Xu, Chang Xu

On one hand, massive trainable parameters significantly enhance the performance of these deep networks.

Discernible Image Compression

no code implementations17 Feb 2020 Zhaohui Yang, Yunhe Wang, Chang Xu, Peng Du, Chao Xu, Chunjing Xu, Qi Tian

Experiments on benchmarks demonstrate that images compressed by using the proposed method can also be well recognized by subsequent visual recognition and detection models.

Image Compression Object Detection

On Positive-Unlabeled Classification in GAN

1 code implementation CVPR 2020 Tianyu Guo, Chang Xu, Jiajun Huang, Yunhe Wang, Boxin Shi, Chao Xu, DaCheng Tao

In contrast, it is more reasonable to treat the generated data as unlabeled, which could be positive or negative according to their quality.

Classification General Classification

AdderNet: Do We Really Need Multiplications in Deep Learning?

2 code implementations CVPR 2020 Hanting Chen, Yunhe Wang, Chunjing Xu, Boxin Shi, Chao Xu, Qi Tian, Chang Xu

The widely-used convolutions in deep neural networks are exactly cross-correlation to measure the similarity between input feature and convolution filters, which involves massive multiplications between float values.

Training Object Detectors from Few Weakly-Labeled and Many Unlabeled Images

no code implementations arXiv 2019 Zhaohui Yang, Miaojing Shi, Chao Xu, Vittorio Ferrari, Yannis Avrithis

Weakly-supervised object detection attempts to limit the amount of supervision by dispensing the need for bounding boxes, but still assumes image-level labels on the entire training set.

Ranked #21 on Weakly Supervised Object Detection on PASCAL VOC 2012 test (using extra training data)

Weakly Supervised Object Detection

Learning from Bad Data via Generation

no code implementations NeurIPS 2019 Tianyu Guo, Chang Xu, Boxin Shi, Chao Xu, DaCheng Tao

A worst-case formulation can be developed over this distribution set, and then be interpreted as a generation task in an adversarial manner.

CARS: Continuous Evolution for Efficient Neural Architecture Search

1 code implementation CVPR 2020 Zhaohui Yang, Yunhe Wang, Xinghao Chen, Boxin Shi, Chao Xu, Chunjing Xu, Qi Tian, Chang Xu

Architectures in the population that share parameters within one SuperNet in the latest generation will be tuned over the training dataset with a few epochs.

Neural Architecture Search

Bringing Giant Neural Networks Down to Earth with Unlabeled Data

no code implementations13 Jul 2019 Yehui Tang, Shan You, Chang Xu, Boxin Shi, Chao Xu

Specifically, we exploit the unlabeled data to mimic the classification characteristics of giant networks, so that the original capacity can be preserved nicely.

On the computational complexity of the probabilistic label tree algorithms

no code implementations1 Jun 2019 Robert Busa-Fekete, Krzysztof Dembczynski, Alexander Golovnev, Kalina Jasinska, Mikhail Kuznetsov, Maxim Sviridenko, Chao Xu

First, we show that finding a tree with optimal training cost is NP-complete, nevertheless there are some tractable special cases with either perfect approximation or exact solution that can be obtained in linear time in terms of the number of labels $m$.

Multi-class Classification

Strain engineering of epitaxial oxide heterostructures beyond substrate limitations

no code implementations3 May 2019 Xiong Deng, Chao Chen, Deyang Chen, Xiangbin Cai, Xiaozhe Yin, Chao Xu, Fei Sun, Caiwen Li, Yan Li, Han Xu, Mao Ye, Guo Tian, Zhen Fan, Zhipeng Hou, Minghui Qin, Yu Chen, Zhenlin Luo, Xubing Lu, Guofu Zhou, Lang Chen, Ning Wang, Ye Zhu, Xingsen Gao, Jun-Ming Liu

The limitation of commercially available single-crystal substrates and the lack of continuous strain tunability preclude the ability to take full advantage of strain engineering for further exploring novel properties and exhaustively studying fundamental physics in complex oxides.

Materials Science

Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification

no code implementations8 Apr 2019 Yong Luo, DaCheng Tao, Chang Xu, Chao Xu, Hong Liu, Yonggang Wen

In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several objects (e. g. pedestrian, bicycle and tree) and is properly characterized by multiple visual features (e. g. color, texture and shape).

General Classification Multi-Label Image Classification

Decomposition-Based Transfer Distance Metric Learning for Image Classification

no code implementations8 Apr 2019 Yong Luo, Tongliang Liu, DaCheng Tao, Chao Xu

In particular, DTDML learns a sparse combination of the base metrics to construct the target metric by forcing the target metric to be close to an integration of the source metrics.

Classification General Classification +3

Multi-View Matrix Completion for Multi-Label Image Classification

no code implementations8 Apr 2019 Yong Luo, Tongliang Liu, DaCheng Tao, Chao Xu

Therefore, we propose to weightedly combine the MC outputs of different views, and present the multi-view matrix completion (MVMC) framework for transductive multi-label image classification.

Classification General Classification +4

Multi-View Intact Space Learning

no code implementations4 Apr 2019 Chang Xu, DaCheng Tao, Chao Xu

In this paper, we propose the Multi-view Intact Space Learning (MISL) algorithm, which integrates the encoded complementary information in multiple views to discover a latent intact representation of the data.

MULTI-VIEW LEARNING

Cost-Sensitive Feature Selection by Optimizing F-Measures

no code implementations4 Apr 2019 Meng Liu, Chang Xu, Yong Luo, Chao Xu, Yonggang Wen, DaCheng Tao

Feature selection is beneficial for improving the performance of general machine learning tasks by extracting an informative subset from the high-dimensional features.

Data-Free Learning of Student Networks

3 code implementations ICCV 2019 Hanting Chen, Yunhe Wang, Chang Xu, Zhaohui Yang, Chuanjian Liu, Boxin Shi, Chunjing Xu, Chao Xu, Qi Tian

Learning portable neural networks is very essential for computer vision for the purpose that pre-trained heavy deep models can be well applied on edge devices such as mobile phones and micro sensors.

Neural Network Compression

Learning Student Networks via Feature Embedding

no code implementations17 Dec 2018 Hanting Chen, Yunhe Wang, Chang Xu, Chao Xu, DaCheng Tao

Experiments on benchmark datasets and well-trained networks suggest that the proposed algorithm is superior to state-of-the-art teacher-student learning methods in terms of computational and storage complexity.

Knowledge Distillation

Robust Student Network Learning

no code implementations30 Jul 2018 Tianyu Guo, Chang Xu, Shiyi He, Boxin Shi, Chao Xu, DaCheng Tao

In this way, a portable student network with significantly fewer parameters can achieve a considerable accuracy which is comparable to that of teacher network.

Revisiting Perspective Information for Efficient Crowd Counting

no code implementations CVPR 2019 Miaojing Shi, Zhaohui Yang, Chao Xu, Qijun Chen

Modern crowd counting methods employ deep neural networks to estimate crowd counts via crowd density regressions.

Crowd Counting

AutoEncoder Inspired Unsupervised Feature Selection

1 code implementation23 Oct 2017 Kai Han, Yunhe Wang, Chao Zhang, Chao Li, Chao Xu

High-dimensional data in many areas such as computer vision and machine learning tasks brings in computational and analytical difficulty.

Beyond Filters: Compact Feature Map for Portable Deep Model

1 code implementation ICML 2017 Yunhe Wang, Chang Xu, Chao Xu, DaCheng Tao

The filter is then re-configured to establish the mapping from original input to the new compact feature map, and the resulting network can preserve intrinsic information of the original network with significantly fewer parameters, which not only decreases the online memory for launching CNN but also accelerates the computation speed.

Towards Evolutional Compression

no code implementations25 Jul 2017 Yunhe Wang, Chang Xu, Jiayan Qiu, Chao Xu, DaCheng Tao

In contrast to directly recognizing subtle weights or filters as redundant in a given CNN, this paper presents an evolutionary method to automatically eliminate redundant convolution filters.

Parametric T-Spline Face Morphable Model for Detailed Fitting in Shape Subspace

no code implementations CVPR 2017 Weilong Peng, Zhiyong Feng, Chao Xu, Yong Su

As any pre-learnt subspace is not complete to handle the variety and details of faces and expressions, it covers a limited span of morphing.

Privileged Multi-label Learning

no code implementations25 Jan 2017 Shan You, Chang Xu, Yunhe Wang, Chao Xu, DaCheng Tao

This paper presents privileged multi-label learning (PrML) to explore and exploit the relationship between labels in multi-label learning problems.

Multi-Label Learning

A Logic of Knowing Why

no code implementations21 Sep 2016 Chao Xu, Yanjing Wang, Thomas Studer

When we say "I know why he was late", we know not only the fact that he was late, but also an explanation of this fact.

Streaming View Learning

no code implementations28 Apr 2016 Chang Xu, DaCheng Tao, Chao Xu

An underlying assumption in conventional multi-view learning algorithms is that all views can be simultaneously accessed.

MULTI-VIEW LEARNING

Parts for the Whole: The DCT Norm for Extreme Visual Recovery

no code implementations19 Apr 2016 Yunhe Wang, Chang Xu, Shan You, DaCheng Tao, Chao Xu

Here we study the extreme visual recovery problem, in which over 90\% of pixel values in a given image are missing.

Streaming Label Learning for Modeling Labels on the Fly

no code implementations19 Apr 2016 Shan You, Chang Xu, Yunhe Wang, Chao Xu, DaCheng Tao

The core of SLL is to explore and exploit the relationships between new labels and past labels and then inherit the relationship into hypotheses of labels to boost the performance of new classifiers.

Multi-Label Learning

B-spline Shape from Motion & Shading: An Automatic Free-form Surface Modeling for Face Reconstruction

no code implementations21 Jan 2016 Weilong Peng, Zhiyong Feng, Chao Xu

In this paper, B-spline Shape from Motion and Shading (BsSfMS) is proposed to reconstruct continuous B-spline surface for multi-view face images, according to an assumption that shading and motion information in the images contain 1st- and 0th-order derivative of B-spline face respectively.

Face Reconstruction

Tensor Canonical Correlation Analysis for Multi-view Dimension Reduction

3 code implementations9 Feb 2015 Yong Luo, DaCheng Tao, Yonggang Wen, Kotagiri Ramamohanarao, Chao Xu

As a consequence, the high order correlation information contained in the different views is explored and thus a more reliable common subspace shared by all features can be obtained.

Dimensionality Reduction MULTI-VIEW LEARNING

Bi-objective Optimization for Robust RGB-D Visual Odometry

no code implementations27 Nov 2014 Tao Han, Chao Xu, Ryan Loxton, Lei Xie

This paper considers a new bi-objective optimization formulation for robust RGB-D visual odometry.

Visual Odometry

Local Rademacher Complexity for Multi-label Learning

no code implementations26 Oct 2014 Chang Xu, Tongliang Liu, DaCheng Tao, Chao Xu

We analyze the local Rademacher complexity of empirical risk minimization (ERM)-based multi-label learning algorithms, and in doing so propose a new algorithm for multi-label learning.

Multi-Label Learning

A Survey on Multi-view Learning

no code implementations20 Apr 2013 Chang Xu, DaCheng Tao, Chao Xu

Notably, co-training style algorithms train alternately to maximize the mutual agreement on two distinct views of the data; multiple kernel learning algorithms exploit kernels that naturally correspond to different views and combine kernels either linearly or non-linearly to improve learning performance; and subspace learning algorithms aim to obtain a latent subspace shared by multiple views by assuming that the input views are generated from this latent subspace.

MULTI-VIEW LEARNING

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