Tongliang Liu

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University of Technology Sydney ∙ The University of Sydney ∙ UNSW

Lecturer in machine learning at the School of Information Technologies, The University of Sydney. He received the BEng degree in electronic engineering and information science from the University of Science and Technology of China, and the PhD degree from the University of Technology Sydney. From October 2015 to March 2016, he was a visiting PhD student with Barcelona Graduate School of Economics (Barcelona GSE) and the Department of Economics at Pompeu Fabra University, Spain.

Featured Co-authors

Yang Liu
344 publications
Dacheng Tao
281 publications
Wei Liu
260 publications
Xin Wang
207 publications
Masashi Sugiyama
177 publications
Bernhard Schölkopf
166 publications
Jie Yang
116 publications
Jie Li
108 publications
Wanli Ouyang
100 publications
Chang Xu
93 publications
Jian Yang
89 publications

PI-GNN: A Novel Perspective on Semi-Supervised Node Classification against Noisy Labels

Semi-supervised node classification, as a fundamental problem in graph l...
06/14/2021 ∙ by Xuefeng Du, et al. ∙ 0 ∙ share

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TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation

In few-shot domain adaptation (FDA), classifiers for the target domain a...
06/11/2021 ∙ by Haoang Chi, et al. ∙ 0 ∙ share

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KRADA: Known-region-aware Domain Alignment for Open World Semantic Segmentation

In semantic segmentation, we aim to train a pixel-level classifier to as...
06/11/2021 ∙ by Chenhong Zhou, et al. ∙ 0 ∙ share

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Adversarial Robustness through the Lens of Causality

The adversarial vulnerability of deep neural networks has attracted sign...
06/11/2021 ∙ by Yonggang Zhang, et al. ∙ 0 ∙ share

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Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training

Deep neural networks (DNNs) are vulnerable to adversarial noise. A range...
06/10/2021 ∙ by Dawei Zhou, et al. ∙ 0 ∙ share

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Towards Defending against Adversarial Examples via Attack-Invariant Features

Deep neural networks (DNNs) are vulnerable to adversarial noise. Their a...
06/09/2021 ∙ by Dawei Zhou, et al. ∙ 0 ∙ share

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Reliable Adversarial Distillation with Unreliable Teachers

In ordinary distillation, student networks are trained with soft labels ...
06/09/2021 ∙ by Jianing Zhu, et al. ∙ 0 ∙ share

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Instance Correction for Learning with Open-set Noisy Labels

The problem of open-set noisy labels denotes that part of training data ...
06/01/2021 ∙ by Xiaobo Xia, et al. ∙ 0 ∙ share

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Sample Selection with Uncertainty of Losses for Learning with Noisy Labels

In learning with noisy labels, the sample selection approach is very pop...
06/01/2021 ∙ by Xiaobo Xia, et al. ∙ 0 ∙ share

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NoiLIn: Do Noisy Labels Always Hurt Adversarial Training?

Adversarial training (AT) based on minimax optimization is a popular lea...
05/31/2021 ∙ by Jingfeng Zhang, et al. ∙ 0 ∙ share

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Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition

In the real world, medical datasets often exhibit a long-tailed data dis...
04/22/2021 ∙ by Lie Ju, et al. ∙ 14 ∙ share

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Removing Adversarial Noise in Class Activation Feature Space

Deep neural networks (DNNs) are vulnerable to adversarial noise. Preproc...
04/19/2021 ∙ by Dawei Zhou, et al. ∙ 0 ∙ share

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Learning with Group Noise

Machine learning in the context of noise is a challenging but practical ...
03/17/2021 ∙ by Qizhou Wang, et al. ∙ 0 ∙ share

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A Machine Learning Approach for Predicting Human Preference for Graph Layouts

Understanding what graph layout human prefer and why they prefer is sign...
03/01/2021 ∙ by Shijun Cai, et al. ∙ 0 ∙ share

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Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation

Deep neural networks are known to be data-driven and label noise can hav...
02/28/2021 ∙ by Lie Ju, et al. ∙ 0 ∙ share

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Meta Discovery: Learning to Discover Novel Classes given Very Limited Data

In learning to discover novel classes(L2DNC), we are given labeled data ...
02/08/2021 ∙ by Haoang Chi, et al. ∙ 0 ∙ share

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Understanding the Interaction of Adversarial Training with Noisy Labels

Noisy labels (NL) and adversarial examples both undermine trained models...
02/06/2021 ∙ by Jianing Zhu, et al. ∙ 0 ∙ share

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Provably End-to-end Label-Noise Learning without Anchor Points

In label-noise learning, the transition matrix plays a key role in build...
02/04/2021 ∙ by Xuefeng Li, et al. ∙ 0 ∙ share

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Learning Diverse-Structured Networks for Adversarial Robustness

In adversarial training (AT), the main focus has been the objective and ...
02/03/2021 ∙ by Xuefeng Du, et al. ∙ 0 ∙ share

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Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model

The drastic increase of data quantity often brings the severe decrease o...
01/14/2021 ∙ by Qizhou Wang, et al. ∙ 0 ∙ share

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A Second-Order Approach to Learning with Instance-Dependent Label Noise

The presence of label noise often misleads the training of deep neural n...
12/22/2020 ∙ by Zhaowei Zhu, et al. ∙ 0 ∙ share

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Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels

The label noise transition matrix T, reflecting the probabilities that t...
12/02/2020 ∙ by Xiaobo Xia, et al. ∙ 0 ∙ share

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A Survey of Label-noise Representation Learning: Past, Present and Future

Classical machine learning implicitly assumes that labels of the trainin...
11/09/2020 ∙ by Bo Han, et al. ∙ 0 ∙ share

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Maximum Mean Discrepancy is Aware of Adversarial Attacks

The maximum mean discrepancy (MMD) test, as a representative two-sample ...
10/22/2020 ∙ by Ruize Gao, et al. ∙ 0 ∙ share

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Experimental Quantum Generative Adversarial Networks for Image Generation

Quantum machine learning is expected to be one of the first practical ap...
10/13/2020 ∙ by He-Liang Huang, et al. ∙ 0 ∙ share

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Quantum differentially private sparse regression learning

Differentially private (DP) learning, which aims to accurately extract p...
07/23/2020 ∙ by Yuxuan Du, et al. ∙ 0 ∙ share

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Weakly Supervised Temporal Action Localization with Segment-Level Labels

Temporal action localization presents a trade-off between test performan...
07/03/2020 ∙ by Xinpeng Ding, et al. ∙ 0 ∙ share

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Parts-dependent Label Noise: Towards Instance-dependent Label Noise

Learning with the instance-dependent label noise is challenging, because...
06/14/2020 ∙ by Xiaobo Xia, et al. ∙ 0 ∙ share

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Class2Simi: A New Perspective on Learning with Label Noise

Label noise is ubiquitous in the era of big data. Deep learning algorith...
06/14/2020 ∙ by Songhua Wu, et al. ∙ 0 ∙ share

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Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning

The transition matrix, denoting the transition relationship from clean l...
06/14/2020 ∙ by Yu Yao, et al. ∙ 0 ∙ share

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Repulsive Mixture Models of Exponential Family PCA for Clustering

The mixture extension of exponential family principal component analysis...
04/07/2020 ∙ by Maoying Qiao, et al. ∙ 0 ∙ share

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Quantum noise protects quantum classifiers against adversaries

Noise in quantum information processing is often viewed as a disruptive ...
03/20/2020 ∙ by Yuxuan Du, et al. ∙ 0 ∙ share

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Multi-Class Classification from Noisy-Similarity-Labeled Data

A similarity label indicates whether two instances belong to the same cl...
02/16/2020 ∙ by Songhua Wu, et al. ∙ 9 ∙ share

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Towards Mixture Proportion Estimation without Irreducibility

Mixture proportion estimation (MPE) is a fundamental problem of practica...
02/10/2020 ∙ by Yu Yao, et al. ∙ 1 ∙ share

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Confidence Scores Make Instance-dependent Label-noise Learning Possible

Learning with noisy labels has drawn a lot of attention. In this area, m...
01/11/2020 ∙ by Antonin Berthon, et al. ∙ 4 ∙ share

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A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection

Deep learning-based computer vision is usually data-hungry. Many researc...
12/15/2019 ∙ by Zhe Chen, et al. ∙ 23 ∙ share

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Continuous Dropout

Dropout has been proven to be an effective algorithm for training robust...
11/28/2019 ∙ by Xu Shen, et al. ∙ 16 ∙ share

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Where is the Bottleneck of Adversarial Learning with Unlabeled Data?

Deep neural networks (DNNs) are incredibly brittle due to adversarial ex...
11/20/2019 ∙ by Jingfeng Zhang, et al. ∙ 0 ∙ share

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Towards Digital Retina in Smart Cities: A Model Generation, Utilization and Communication Paradigm

The digital retina in smart cities is to select what the City Eye tells ...
07/31/2019 ∙ by Yihang Lou, et al. ∙ 0 ∙ share

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A Quantum-inspired Algorithm for General Minimum Conical Hull Problems

A wide range of fundamental machine learning tasks that are addressed by...
07/16/2019 ∙ by Yuxuan Du, et al. ∙ 0 ∙ share

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Truncated Cauchy Non-negative Matrix Factorization

Non-negative matrix factorization (NMF) minimizes the Euclidean distance...
06/02/2019 ∙ by Naiyang Guan, et al. ∙ 0 ∙ share

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Are Anchor Points Really Indispensable in Label-Noise Learning?

In label-noise learning, noise transition matrix, denoting the probabili...
06/01/2019 ∙ by Xiaobo Xia, et al. ∙ 0 ∙ share

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Orthogonal Deep Neural Networks

In this paper, we introduce the algorithms of Orthogonal Deep Neural Net...
05/15/2019 ∙ by Kui Jia, et al. ∙ 6 ∙ share

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DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs

Due to the high storage and search efficiency, hashing has become preval...
05/09/2019 ∙ by Erkun Yang, et al. ∙ 0 ∙ share

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dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs

Objective assessment of image quality is fundamentally important in many...
04/13/2019 ∙ by Kede Ma, et al. ∙ 0 ∙ share

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Transferring Knowledge Fragments for Learning Distance Metric from A Heterogeneous Domain

The goal of transfer learning is to improve the performance of target le...
04/08/2019 ∙ by Yong Luo, et al. ∙ 14 ∙ share

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Adaptive Morphological Reconstruction for Seeded Image Segmentation

Morphological reconstruction (MR) is often employed by seeded image segm...
04/08/2019 ∙ by Tao Lei, et al. ∙ 0 ∙ share

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Multi-View Matrix Completion for Multi-Label Image Classification

There is growing interest in multi-label image classification due to its...
04/08/2019 ∙ by Yong Luo, et al. ∙ 0 ∙ share

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Decomposition-Based Transfer Distance Metric Learning for Image Classification

Distance metric learning (DML) is a critical factor for image analysis a...
04/08/2019 ∙ by Yong Luo, et al. ∙ 0 ∙ share

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Fast Supervised Discrete Hashing

Learning-based hashing algorithms are "hot topics" because they can grea...
04/07/2019 ∙ by Jie Gui, et al. ∙ 0 ∙ share

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