Search Results for author: Hang Su

Found 78 papers, 28 papers with code

Defense Against Adversarial Attacks via Controlling Gradient Leaking on Embedded Manifolds

no code implementations ECCV 2020 Yueru Li, Shuyu Cheng, Hang Su, Jun Zhu

Based on our investigation, we further present a new robust learning algorithm which encourages a larger gradient component in the tangent space of data manifold, suppressing the gradient leaking phenomenon consequently.

You Cannot Easily Catch Me: A Low-Detectable Adversarial Patch for Object Detectors

no code implementations30 Sep 2021 Zijian Zhu, Hang Su, Chang Liu, Wenzhao Xiang, Shibao Zheng

Fortunately, most existing adversarial patches can be outwitted, disabled and rejected by a simple classification network called an adversarial patch detector, which distinguishes adversarial patches from original images.

Self-Driving Cars

Query-based Adversarial Attacks on Graph with Fake Nodes

no code implementations27 Sep 2021 Zhengyi Wang, Zhongkai Hao, Hang Su, Jun Zhu

To address these issues, we proposed Cluster Attack, a novel adversarial attack by introducing a set of fake nodes to the original graph which can mislead the classification on certain victim nodes.

Adversarial Attack Image Classification

Improving Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator

no code implementations13 Sep 2021 Wenzhao Xiang, Hang Su, Chang Liu, Yandong Guo, Shibao Zheng

Adversarial examples can deceive a deep neural network (DNN) by significantly altering its response with imperceptible perturbations, which poses new potential vulnerabilities as the growing ubiquity of DNNs.

Adversarial Attack Affine Transformation

Tianshou: a Highly Modularized Deep Reinforcement Learning Library

1 code implementation29 Jul 2021 Jiayi Weng, Huayu Chen, Dong Yan, Kaichao You, Alexis Duburcq, Minghao Zhang, Hang Su, Jun Zhu

We present Tianshou, a highly modularized python library for deep reinforcement learning (DRL) that uses PyTorch as its backend.

Query2Label: A Simple Transformer Way to Multi-Label Classification

1 code implementation22 Jul 2021 Shilong Liu, Lei Zhang, Xiao Yang, Hang Su, Jun Zhu

The use of Transformer is rooted in the need of extracting local discriminative features adaptively for different labels, which is a strongly desired property due to the existence of multiple objects in one image.

Classification Multi-Label Classification

Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI

no code implementations16 Jul 2021 Quanshi Zhang, Tian Han, Lixin Fan, Zhanxing Zhu, Hang Su, Ying Nian Wu, Jie Ren, Hao Zhang

This workshop pays a special interest in theoretic foundations, limitations, and new application trends in the scope of XAI.

Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks

no code implementations5 Jul 2021 Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu

Transfer-based adversarial attacks can effectively evaluate model robustness in the black-box setting.

Understanding Adversarial Attacks on Observations in Deep Reinforcement Learning

no code implementations30 Jun 2021 You Qiaoben, Chengyang Ying, Xinning Zhou, Hang Su, Jun Zhu, Bo Zhang

Following the analysis of the function space, we design a generic two-stage framework in the subspace where the adversary lures the agent to a target trajectory or a deceptive policy.

Accumulative Poisoning Attacks on Real-time Data

1 code implementation18 Jun 2021 Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, Jun Zhu

Collecting training data from untrusted sources exposes machine learning services to poisoning adversaries, who maliciously manipulate training data to degrade the model accuracy.

Federated Learning

Exploring Memorization in Adversarial Training

no code implementations3 Jun 2021 Yinpeng Dong, Ke Xu, Xiao Yang, Tianyu Pang, Zhijie Deng, Hang Su, Jun Zhu

In this paper, we investigate the memorization effect in adversarial training (AT) for promoting a deeper understanding of capacity, convergence, generalization, and especially robust overfitting of adversarially trained classifiers.

Adversarial Training with Rectified Rejection

1 code implementation31 May 2021 Tianyu Pang, Huishuai Zhang, Di He, Yinpeng Dong, Hang Su, Wei Chen, Jun Zhu, Tie-Yan Liu

Adversarial training (AT) is one of the most effective strategies for promoting model robustness, whereas even the state-of-the-art adversarially trained models struggle to exceed 65% robust test accuracy on CIFAR-10 without additional data, which is far from practical.

Unsupervised Part Segmentation through Disentangling Appearance and Shape

no code implementations CVPR 2021 Shilong Liu, Lei Zhang, Xiao Yang, Hang Su, Jun Zhu

We study the problem of unsupervised discovery and segmentation of object parts, which, as an intermediate local representation, are capable of finding intrinsic object structure and providing more explainable recognition results.

Semantic Segmentation

Automated Decision-based Adversarial Attacks

no code implementations9 May 2021 Qi-An Fu, Yinpeng Dong, Hang Su, Jun Zhu

Deep learning models are vulnerable to adversarial examples, which can fool a target classifier by imposing imperceptible perturbations onto natural examples.

Adversarial Attack Program Synthesis

Dissecting User-Perceived Latency of On-Device E2E Speech Recognition

no code implementations6 Apr 2021 Yuan Shangguan, Rohit Prabhavalkar, Hang Su, Jay Mahadeokar, Yangyang Shi, Jiatong Zhou, Chunyang Wu, Duc Le, Ozlem Kalinli, Christian Fuegen, Michael L. Seltzer

As speech-enabled devices such as smartphones and smart speakers become increasingly ubiquitous, there is growing interest in building automatic speech recognition (ASR) systems that can run directly on-device; end-to-end (E2E) speech recognition models such as recurrent neural network transducers and their variants have recently emerged as prime candidates for this task.

automatic-speech-recognition Speech Recognition

LiBRe: A Practical Bayesian Approach to Adversarial Detection

1 code implementation CVPR 2021 Zhijie Deng, Xiao Yang, Shizhen Xu, Hang Su, Jun Zhu

Despite their appealing flexibility, deep neural networks (DNNs) are vulnerable against adversarial examples.

Adversarial Defense

Black-box Detection of Backdoor Attacks with Limited Information and Data

no code implementations ICCV 2021 Yinpeng Dong, Xiao Yang, Zhijie Deng, Tianyu Pang, Zihao Xiao, Hang Su, Jun Zhu

Although deep neural networks (DNNs) have made rapid progress in recent years, they are vulnerable in adversarial environments.

QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval

no code implementations CVPR 2021 Xiaodan Li, Jinfeng Li, Yuefeng Chen, Shaokai Ye, Yuan He, Shuhui Wang, Hang Su, Hui Xue

Comprehensive experiments show that the proposed attack achieves a high attack success rate with few queries against the image retrieval systems under the black-box setting.

Image Classification Image Retrieval

Growth, Electronic Structure and Superconductivity of Ultrathin Epitaxial CoSi2 Films

no code implementations21 Jan 2021 Yuan Fang, Ding Wang, Peng Li, Hang Su, Tian Le, Yi Wu, Guo-Wei Yang, Hua-Li Zhang, Zhi-Guang Xiao, Yan-Qiu Sun, Si-Yuan Hong, Yan-Wu Xie, Huan-Hua Wang, Chao Cao, Xin Lu, Hui-Qiu Yuan, Yang Liu

We report growth, electronic structure and superconductivity of ultrathin epitaxial CoSi2 films on Si(111).

Mesoscale and Nanoscale Physics

Adaptive N-step Bootstrapping with Off-policy Data

no code implementations1 Jan 2021 Guan Wang, Dong Yan, Hang Su, Jun Zhu

In this work, we point out that the optimal value of n actually differs on each data point, while the fixed value n is a rough average of them.

Atari Games

Composite Adversarial Attacks

1 code implementation10 Dec 2020 Xiaofeng Mao, Yuefeng Chen, Shuhui Wang, Hang Su, Yuan He, Hui Xue

Adversarial attack is a technique for deceiving Machine Learning (ML) models, which provides a way to evaluate the adversarial robustness.

Adversarial Attack

Robust Unsupervised Small Area Change Detection from SAR Imagery Using Deep Learning

1 code implementation22 Nov 2020 Xinzheng Zhang, Hang Su, Ce Zhang, Xiaowei Gu, Xiaoheng Tan, Peter M. Atkinson

In this paper, a robust unsupervised approach is proposed for small area change detection from multi-temporal SAR images using deep learning.

Alignment Restricted Streaming Recurrent Neural Network Transducer

no code implementations5 Nov 2020 Jay Mahadeokar, Yuan Shangguan, Duc Le, Gil Keren, Hang Su, Thong Le, Ching-Feng Yeh, Christian Fuegen, Michael L. Seltzer

There is a growing interest in the speech community in developing Recurrent Neural Network Transducer (RNN-T) models for automatic speech recognition (ASR) applications.

automatic-speech-recognition Speech Recognition

Bi-level Score Matching for Learning Energy-based Latent Variable Models

1 code implementation NeurIPS 2020 Fan Bao, Chongxuan Li, Kun Xu, Hang Su, Jun Zhu, Bo Zhang

This paper presents a bi-level score matching (BiSM) method to learn EBLVMs with general structures by reformulating SM as a bi-level optimization problem.

Latent Variable Models Stochastic Optimization

Bag of Tricks for Adversarial Training

2 code implementations ICLR 2021 Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, Jun Zhu

Adversarial training (AT) is one of the most effective strategies for promoting model robustness.

Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters

1 code implementation ECCV 2020 Haoyu Liang, Zhihao Ouyang, Yuyuan Zeng, Hang Su, Zihao He, Shu-Tao Xia, Jun Zhu, Bo Zhang

Most existing works attempt post-hoc interpretation on a pre-trained model, while neglecting to reduce the entanglement underlying the model.

Object Localization

RobFR: Benchmarking Adversarial Robustness on Face Recognition

2 code implementations8 Jul 2020 Xiao Yang, Dingcheng Yang, Yinpeng Dong, Hang Su, Wenjian Yu, Jun Zhu

Based on large-scale evaluations, the commercial FR API services fail to exhibit acceptable performance on robustness evaluation, and we also draw several important conclusions for understanding the adversarial robustness of FR models and providing insights for the design of robust FR models.

Face Recognition

Towards Face Encryption by Generating Adversarial Identity Masks

1 code implementation ICCV 2021 Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu, Yuefeng Chen, Hui Xue

As billions of personal data being shared through social media and network, the data privacy and security have drawn an increasing attention.

Face Recognition

Triple Memory Networks: a Brain-Inspired Method for Continual Learning

no code implementations6 Mar 2020 Liyuan Wang, Bo Lei, Qian Li, Hang Su, Jun Zhu, Yi Zhong

Continual acquisition of novel experience without interfering previously learned knowledge, i. e. continual learning, is critical for artificial neural networks, but limited by catastrophic forgetting.

class-incremental learning Hippocampus +1

A Robust Imbalanced SAR Image Change Detection Approach Based on Deep Difference Image and PCANet

no code implementations3 Mar 2020 Xinzheng Zhang, Hang Su, Ce Zhang, Peter M. Atkinson, Xiaoheng Tan, Xiaoping Zeng, Xin Jian

Parallel FCM are utilized on these two mapped DDIs to obtain three types of pseudo-label pixels, namely, changed pixels, unchanged pixels, and intermediate pixels.

User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization

no code implementations29 Feb 2020 Kang Wei, Jun Li, Ming Ding, Chuan Ma, Hang Su, Bo Zhang, H. Vincent Poor

According to our analysis, the UDP framework can realize $(\epsilon_{i}, \delta_{i})$-LDP for the $i$-th MT with adjustable privacy protection levels by varying the variances of the artificial noise processes.

Federated Learning

Boosting Adversarial Training with Hypersphere Embedding

1 code implementation NeurIPS 2020 Tianyu Pang, Xiao Yang, Yinpeng Dong, Kun Xu, Jun Zhu, Hang Su

Adversarial training (AT) is one of the most effective defenses against adversarial attacks for deep learning models.

Representation Learning

Adversarial Distributional Training for Robust Deep Learning

1 code implementation NeurIPS 2020 Yinpeng Dong, Zhijie Deng, Tianyu Pang, Hang Su, Jun Zhu

Adversarial training (AT) is among the most effective techniques to improve model robustness by augmenting training data with adversarial examples.

OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples

no code implementations8 Feb 2020 Changjian Chen, Jun Yuan, Yafeng Lu, Yang Liu, Hang Su, Songtao Yuan, Shixia Liu

To better analyze and understand the OoD samples in context, we have developed a novel kNN-based grid layout algorithm motivated by Hall's theorem.

Analyzing the Noise Robustness of Deep Neural Networks

no code implementations26 Jan 2020 Kelei Cao, Mengchen Liu, Hang Su, Jing Wu, Jun Zhu, Shixia Liu

The key is to compare and analyze the datapaths of both the adversarial and normal examples.

Adversarial Attack

SVQN: Sequential Variational Soft Q-Learning Networks

no code implementations ICLR 2020 Shiyu Huang, Hang Su, Jun Zhu, Ting Chen

Partially Observable Markov Decision Processes (POMDPs) are popular and flexible models for real-world decision-making applications that demand the information from past observations to make optimal decisions.

Decision Making Q-Learning

Adversarially Robust Neural Networks via Optimal Control: Bridging Robustness with Lyapunov Stability

no code implementations ICLR 2020 Zhiyang Chen, Hang Su

From this viewpoint, training neural nets is equivalent to finding an optimal control of the discrete dynamical system, which allows one to utilize methods of successive approximations, an optimal control algorithm based on Pontryagin's maximum principle, to train neural nets.

Benchmarking Adversarial Robustness

no code implementations26 Dec 2019 Yinpeng Dong, Qi-An Fu, Xiao Yang, Tianyu Pang, Hang Su, Zihao Xiao, Jun Zhu

Deep neural networks are vulnerable to adversarial examples, which becomes one of the most important research problems in the development of deep learning.

Adversarial Attack Image Classification

Biometrics Recognition Using Deep Learning: A Survey

no code implementations30 Nov 2019 Shervin Minaee, Amirali Abdolrashidi, Hang Su, Mohammed Bennamoun, David Zhang

Deep learning-based models have been very successful in achieving state-of-the-art results in many of the computer vision, speech recognition, and natural language processing tasks in the last few years.

Gait Recognition Speech Recognition

Interpretable Disentanglement of Neural Networks by Extracting Class-Specific Subnetwork

no code implementations7 Oct 2019 Yulong Wang, Xiaolin Hu, Hang Su

We also apply extracted subnetworks in visual explanation and adversarial example detection tasks by merely replacing the original full model with class-specific subnetworks.

Pruning from Scratch

1 code implementation27 Sep 2019 Yulong Wang, Xiaolu Zhang, Lingxi Xie, Jun Zhou, Hang Su, Bo Zhang, Xiaolin Hu

Network pruning is an important research field aiming at reducing computational costs of neural networks.

Network Pruning

Improving Black-box Adversarial Attacks with a Transfer-based Prior

2 code implementations NeurIPS 2019 Shuyu Cheng, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu

We consider the black-box adversarial setting, where the adversary has to generate adversarial perturbations without access to the target models to compute gradients.

Boosting Generative Models by Leveraging Cascaded Meta-Models

1 code implementation11 May 2019 Fan Bao, Hang Su, Jun Zhu

Besides, our framework can be extended to semi-supervised boosting, where the boosted model learns a joint distribution of data and labels.

Pixel-Adaptive Convolutional Neural Networks

1 code implementation CVPR 2019 Hang Su, Varun Jampani, Deqing Sun, Orazio Gallo, Erik Learned-Miller, Jan Kautz

In addition, we also demonstrate that PAC can be used as a drop-in replacement for convolution layers in pre-trained networks, resulting in consistent performance improvements.

Efficient Decision-based Black-box Adversarial Attacks on Face Recognition

no code implementations CVPR 2019 Yinpeng Dong, Hang Su, Baoyuan Wu, Zhifeng Li, Wei Liu, Tong Zhang, Jun Zhu

In this paper, we evaluate the robustness of state-of-the-art face recognition models in the decision-based black-box attack setting, where the attackers have no access to the model parameters and gradients, but can only acquire hard-label predictions by sending queries to the target model.

Face Recognition

Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks

1 code implementation CVPR 2019 Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu

In this paper, we propose a translation-invariant attack method to generate more transferable adversarial examples against the defense models.


Reward Shaping via Meta-Learning

no code implementations27 Jan 2019 Haosheng Zou, Tongzheng Ren, Dong Yan, Hang Su, Jun Zhu

Reward shaping is one of the most effective methods to tackle the crucial yet challenging problem of credit assignment in Reinforcement Learning (RL).


Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples

no code implementations25 Jan 2019 Yinpeng Dong, Fan Bao, Hang Su, Jun Zhu

3) We propose to improve the consistency of neurons on adversarial example subset by an adversarial training algorithm with a consistent loss.

Analyzing the Noise Robustness of Deep Neural Networks

no code implementations9 Oct 2018 Mengchen Liu, Shixia Liu, Hang Su, Kelei Cao, Jun Zhu

Deep neural networks (DNNs) are vulnerable to maliciously generated adversarial examples.

Deep Structured Generative Models

no code implementations10 Jul 2018 Kun Xu, Haoyu Liang, Jun Zhu, Hang Su, Bo Zhang

Deep generative models have shown promising results in generating realistic images, but it is still non-trivial to generate images with complicated structures.

Open Logo Detection Challenge

2 code implementations5 Jul 2018 Hang Su, Xiatian Zhu, Shaogang Gong

In this work, we introduce a more realistic and challenging logo detection setting, called Open Logo Detection.

Interpret Neural Networks by Identifying Critical Data Routing Paths

no code implementations CVPR 2018 Yulong Wang, Hang Su, Bo Zhang, Xiaolin Hu

Interpretability of a deep neural network aims to explain the rationale behind its decisions and enable the users to understand the intelligent agents, which has become an important issue due to its importance in practical applications.

Robust and Efficient Graph Correspondence Transfer for Person Re-identification

no code implementations15 May 2018 Qin Zhou, Heng Fan, Hua Yang, Hang Su, Shibao Zheng, Shuang Wu, Haibin Ling

To address this problem, in this paper, we present a robust and efficient graph correspondence transfer (REGCT) approach for explicit spatial alignment in Re-ID.

Graph Matching Person Re-Identification

Scalable Deep Learning Logo Detection

2 code implementations30 Mar 2018 Hang Su, Shaogang Gong, Xiatian Zhu

Existing logo detection methods usually consider a small number of logo classes and limited images per class with a strong assumption of requiring tedious object bounding box annotations, therefore not scalable to real-world dynamic applications.

Incremental Learning

Weighted Bilinear Coding over Salient Body Parts for Person Re-identification

no code implementations22 Mar 2018 Zhigang Chang, Qin Zhou, Heng Fan, Hang Su, Hua Yang, Shibao Zheng, Haibin Ling

Meanwhile, a weighting scheme is applied on the bilinear coding to adaptively adjust the weights of local features at different locations based on their importance in recognition, further improving the discriminability of feature aggregation.

Person Re-Identification

Sparse Adversarial Perturbations for Videos

1 code implementation7 Mar 2018 Xingxing Wei, Jun Zhu, Hang Su

Although adversarial samples of deep neural networks (DNNs) have been intensively studied on static images, their extensions in videos are never explored.

Action Recognition

SPLATNet: Sparse Lattice Networks for Point Cloud Processing

2 code implementations CVPR 2018 Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, Jan Kautz

We present a network architecture for processing point clouds that directly operates on a collection of points represented as a sparse set of samples in a high-dimensional lattice.

3D Part Segmentation 3D Semantic Segmentation

Understanding Human Behaviors in Crowds by Imitating the Decision-Making Process

no code implementations25 Jan 2018 Haosheng Zou, Hang Su, Shihong Song, Jun Zhu

Crowd behavior understanding is crucial yet challenging across a wide range of applications, since crowd behavior is inherently determined by a sequential decision-making process based on various factors, such as the pedestrians' own destinations, interaction with nearby pedestrians and anticipation of upcoming events.

Decision Making Imitation Learning

Detecting Institutional Dialog Acts in Police Traffic Stops

no code implementations TACL 2018 Vinodkumar Prabhakaran, Camilla Griffiths, Hang Su, Prateek Verma, Nelson Morgan, Jennifer L. Eberhardt, Dan Jurafsky

We apply computational dialog methods to police body-worn camera footage to model conversations between police officers and community members in traffic stops.

Speech Recognition

Learning to Write Stylized Chinese Characters by Reading a Handful of Examples

no code implementations6 Dec 2017 Danyang Sun, Tongzheng Ren, Chongxun Li, Hang Su, Jun Zhu

Automatically writing stylized Chinese characters is an attractive yet challenging task due to its wide applicabilities.

Incorporating External Knowledge to Answer Open-Domain Visual Questions with Dynamic Memory Networks

no code implementations3 Dec 2017 Guohao Li, Hang Su, Wenwu Zhu

To address this issue, we propose a novel framework which endows the model capabilities in answering more complex questions by leveraging massive external knowledge with dynamic memory networks.

Question Answering Visual Question Answering

Boosting Adversarial Attacks with Momentum

5 code implementations CVPR 2018 Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, Jianguo Li

To further improve the success rates for black-box attacks, we apply momentum iterative algorithms to an ensemble of models, and show that the adversarially trained models with a strong defense ability are also vulnerable to our black-box attacks.

Adversarial Attack

End-To-End Face Detection and Cast Grouping in Movies Using Erdos-Renyi Clustering

no code implementations ICCV 2017 SouYoung Jin, Hang Su, Chris Stauffer, Erik Learned-Miller

We introduce a novel verification method, rank-1 counts verification, that has this property, and use it in a link-based clustering scheme.

Face Detection

End-to-end Face Detection and Cast Grouping in Movies Using Erdős-Rényi Clustering

no code implementations7 Sep 2017 SouYoung Jin, Hang Su, Chris Stauffer, Erik Learned-Miller

We introduce a novel verification method, rank-1 counts verification, that has this property, and use it in a link-based clustering scheme.

Face Detection

Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples

no code implementations18 Aug 2017 Yinpeng Dong, Hang Su, Jun Zhu, Fan Bao

We find that: (1) the neurons in DNNs do not truly detect semantic objects/parts, but respond to objects/parts only as recurrent discriminative patches; (2) deep visual representations are not robust distributed codes of visual concepts because the representations of adversarial images are largely not consistent with those of real images, although they have similar visual appearance, both of which are different from previous findings.

Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization

1 code implementation3 Aug 2017 Yinpeng Dong, Renkun Ni, Jianguo Li, Yurong Chen, Jun Zhu, Hang Su

This procedure can greatly compensate the quantization error and thus yield better accuracy for low-bit DNNs.


SAM: Semantic Attribute Modulation for Language Modeling and Style Variation

no code implementations1 Jul 2017 Wenbo Hu, Lifeng Hua, Lei LI, Hang Su, Tian Wang, Ning Chen, Bo Zhang

This paper presents a Semantic Attribute Modulation (SAM) for language modeling and style variation.

Language Modelling

Improving Interpretability of Deep Neural Networks with Semantic Information

no code implementations CVPR 2017 Yinpeng Dong, Hang Su, Jun Zhu, Bo Zhang

Interpretability of deep neural networks (DNNs) is essential since it enables users to understand the overall strengths and weaknesses of the models, conveys an understanding of how the models will behave in the future, and how to diagnose and correct potential problems.

Action Recognition Video Captioning

Deep Learning Logo Detection with Data Expansion by Synthesising Context

no code implementations29 Dec 2016 Hang Su, Xiatian Zhu, Shaogang Gong

Logo detection in unconstrained images is challenging, particularly when only very sparse labelled training images are accessible due to high labelling costs.

FLASH: Fast Bayesian Optimization for Data Analytic Pipelines

1 code implementation20 Feb 2016 Yuyu Zhang, Mohammad Taha Bahadori, Hang Su, Jimeng Sun

To achieve the best performance, it is often critical to select optimal algorithms and to set appropriate hyperparameters, which requires large computational efforts.

Experiments on Parallel Training of Deep Neural Network using Model Averaging

1 code implementation5 Jul 2015 Hang Su, Haoyu Chen

Data is partitioned and distributed to different nodes for local model updates, and model averaging across nodes is done every few minibatches.

Active Sample Selection and Correction Propagation on a Gradually-Augmented Graph

no code implementations CVPR 2015 Hang Su, Zhaozheng Yin, Takeo Kanade, Seungil Huh

When data have a complex manifold structure or the characteristics of data evolve over time, it is unrealistic to expect a graph-based semi-supervised learning method to achieve flawless classification given a small number of initial annotations.

Classification General Classification

Multi-view Convolutional Neural Networks for 3D Shape Recognition

no code implementations ICCV 2015 Hang Su, Subhransu Maji, Evangelos Kalogerakis, Erik Learned-Miller

A longstanding question in computer vision concerns the representation of 3D shapes for recognition: should 3D shapes be represented with descriptors operating on their native 3D formats, such as voxel grid or polygon mesh, or can they be effectively represented with view-based descriptors?

3D Point Cloud Classification 3D Shape Recognition

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