Tongliang Liu

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University of Technology Sydney

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The University of Sydney

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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

Dacheng Tao
367 publications
Yang Liu
344 publications
Wei Liu
313 publications
Xin Wang
289 publications
Bernhard Schölkopf
211 publications
Masashi Sugiyama
194 publications
Jie Yang
156 publications
Jie Li
142 publications
Wanli Ouyang
126 publications
Chang Xu
122 publications
Jian Yang
121 publications

∙ 06/17/2022

Understanding Robust Overfitting of Adversarial Training and Beyond

Robust overfitting widely exists in adversarial training of deep network...

Chaojian Yu, et al. ∙

∙ 06/16/2022

Multi-scale Cooperative Multimodal Transformers for Multimodal Sentiment Analysis in Videos

Multimodal sentiment analysis in videos is a key task in many real-world...

Lianyang Ma, et al. ∙

∙ 06/11/2022

Bilateral Dependency Optimization: Defending Against Model-inversion Attacks

Through using only a well-trained classifier, model-inversion (MI) attac...

Xiong Peng, et al. ∙

∙ 06/07/2022

Recent Advances for Quantum Neural Networks in Generative Learning

Quantum computers are next-generation devices that hold promise to perfo...

Jinkai Tian, et al. ∙

∙ 06/06/2022

Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation

In label-noise learning, estimating the transition matrix has attracted ...

De Cheng, et al. ∙

∙ 06/04/2022

MSR: Making Self-supervised learning Robust to Aggressive Augmentations

Most recent self-supervised learning methods learn visual representation...

Yingbin Bai, et al. ∙

∙ 05/30/2022

Robust Weight Perturbation for Adversarial Training

Overfitting widely exists in adversarial robust training of deep network...

Chaojian Yu, et al. ∙

∙ 05/27/2022

Counterfactual Fairness with Partially Known Causal Graph

Fair machine learning aims to avoid treating individuals or sub-populati...

Aoqi Zuo, et al. ∙

∙ 05/27/2022

MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models

State-of-the-art causal discovery methods usually assume that the observ...

Erdun Gao, et al. ∙

∙ 05/18/2022

Pluralistic Image Completion with Probabilistic Mixture-of-Experts

Pluralistic image completion focuses on generating both visually realist...

Xiaobo Xia, et al. ∙

∙ 04/15/2022

Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities

Graph neural networks have emerged as a leading architecture for many gr...

Chuang Liu, et al. ∙

∙ 03/29/2022

SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation

This paper studies a practical domain adaptive (DA) semantic segmentatio...

Xiaoqing Guo, et al. ∙

∙ 03/28/2022

Killing Two Birds with One Stone:Efficient and Robust Training of Face Recognition CNNs by Partial FC

Learning discriminative deep feature embeddings by using million-scale i...

Xiang An, et al. ∙

∙ 03/08/2022

Trustable Co-label Learning from Multiple Noisy Annotators

Supervised deep learning depends on massive accurately annotated example...

Shikun Li, et al. ∙

∙ 03/08/2022

Selective-Supervised Contrastive Learning with Noisy Labels

Deep networks have strong capacities of embedding data into latent repre...

Shikun Li, et al. ∙

∙ 02/16/2022

Understanding and Improving Graph Injection Attack by Promoting Unnoticeability

Recently Graph Injection Attack (GIA) emerges as a practical attack scen...

Yongqiang Chen, et al. ∙

∙ 02/11/2022

Invariance Principle Meets Out-of-Distribution Generalization on Graphs

Despite recent developments in using the invariance principle from causa...

Yongqiang Chen, et al. ∙

∙ 01/30/2022

Do We Need to Penalize Variance of Losses for Learning with Label Noise?

Algorithms which minimize the averaged loss have been widely designed fo...

Yexiong Lin, et al. ∙

∙ 12/07/2021

Federated Causal Discovery

Causal discovery aims to learn a causal graph from observational data. T...

Erdun Gao, et al. ∙

∙ 12/02/2021

Transfer Learning in Conversational Analysis through Reusing Preprocessing Data as Supervisors

Conversational analysis systems are trained using noisy human labels and...

Joshua Yee Kim, et al. ∙

∙ 11/30/2021

CRIS: CLIP-Driven Referring Image Segmentation

Referring image segmentation aims to segment a referent via a natural li...

Zhaoqing Wang, et al. ∙

∙ 11/19/2021

Meta Clustering Learning for Large-scale Unsupervised Person Re-identification

Unsupervised Person Re-identification (U-ReID) with pseudo labeling rece...

Xin Jin, et al. ∙

∙ 09/21/2021

Modelling Adversarial Noise for Adversarial Defense

Deep neural networks have been demonstrated to be vulnerable to adversar...

Dawei Zhou, et al. ∙

∙ 09/07/2021

Instance-dependent Label-noise Learning under a Structural Causal Model

Label noise will degenerate the performance of deep learning algorithms ...

Yu Yao, et al. ∙

∙ 07/19/2021

Exploring Set Similarity for Dense Self-supervised Representation Learning

By considering the spatial correspondence, dense self-supervised represe...

Zhaoqing Wang, et al. ∙

∙ 07/10/2021

Kernel Mean Estimation by Marginalized Corrupted Distributions

Estimating the kernel mean in a reproducing kernel Hilbert space is a cr...

Xiaobo Xia, et al. ∙

∙ 07/01/2021

Revisiting Knowledge Distillation: An Inheritance and Exploration Framework

Knowledge Distillation (KD) is a popular technique to transfer knowledge...

Zhen Huang, et al. ∙

∙ 06/30/2021

Understanding and Improving Early Stopping for Learning with Noisy Labels

The memorization effect of deep neural network (DNN) plays a pivotal rol...

Yingbin Bai, et al. ∙

∙ 06/15/2021

Probabilistic Margins for Instance Reweighting in Adversarial Training

Reweighting adversarial data during training has been recently shown to ...

Qizhou Wang, et al. ∙

∙ 06/14/2021

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

Semi-supervised node classification, as a fundamental problem in graph l...

Xuefeng Du, et al. ∙

∙ 06/11/2021

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

In few-shot domain adaptation (FDA), classifiers for the target domain a...

Haoang Chi, et al. ∙

∙ 06/11/2021

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

In semantic segmentation, we aim to train a pixel-level classifier to as...

Chenhong Zhou, et al. ∙

∙ 06/11/2021

Adversarial Robustness through the Lens of Causality

The adversarial vulnerability of deep neural networks has attracted sign...

Yonggang Zhang, et al. ∙

∙ 06/10/2021

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

Deep neural networks (DNNs) are vulnerable to adversarial noise. A range...

Dawei Zhou, et al. ∙

∙ 06/09/2021

Towards Defending against Adversarial Examples via Attack-Invariant Features

Deep neural networks (DNNs) are vulnerable to adversarial noise. Their a...

Dawei Zhou, et al. ∙

∙ 06/09/2021

Reliable Adversarial Distillation with Unreliable Teachers

In ordinary distillation, student networks are trained with soft labels ...

Jianing Zhu, et al. ∙

∙ 06/01/2021

Instance Correction for Learning with Open-set Noisy Labels

The problem of open-set noisy labels denotes that part of training data ...

Xiaobo Xia, et al. ∙

∙ 06/01/2021

Sample Selection with Uncertainty of Losses for Learning with Noisy Labels

In learning with noisy labels, the sample selection approach is very pop...

Xiaobo Xia, et al. ∙

∙ 05/31/2021

NoiLIn: Do Noisy Labels Always Hurt Adversarial Training?

Adversarial training (AT) based on minimax optimization is a popular lea...

Jingfeng Zhang, et al. ∙

∙ 04/22/2021

Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition

In the real world, medical datasets often exhibit a long-tailed data dis...

Lie Ju, et al. ∙

∙ 04/19/2021

Removing Adversarial Noise in Class Activation Feature Space

Deep neural networks (DNNs) are vulnerable to adversarial noise. Preproc...

Dawei Zhou, et al. ∙

∙ 03/17/2021

Learning with Group Noise

Machine learning in the context of noise is a challenging but practical ...

Qizhou Wang, et al. ∙

∙ 03/01/2021

A Machine Learning Approach for Predicting Human Preference for Graph Layouts

Understanding what graph layout human prefer and why they prefer is sign...

Shijun Cai, et al. ∙

∙ 02/28/2021

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...

Lie Ju, et al. ∙

∙ 02/08/2021

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

In learning to discover novel classes(L2DNC), we are given labeled data ...

Haoang Chi, et al. ∙

∙ 02/06/2021

Understanding the Interaction of Adversarial Training with Noisy Labels

Noisy labels (NL) and adversarial examples both undermine trained models...

Jianing Zhu, et al. ∙

∙ 02/04/2021

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

In label-noise learning, the transition matrix plays a key role in build...

Xuefeng Li, et al. ∙

∙ 02/03/2021

Learning Diverse-Structured Networks for Adversarial Robustness

In adversarial training (AT), the main focus has been the objective and ...

Xuefeng Du, et al. ∙

∙ 01/14/2021

Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model

The drastic increase of data quantity often brings the severe decrease o...

Qizhou Wang, et al. ∙

∙ 12/22/2020

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

The presence of label noise often misleads the training of deep neural n...

Zhaowei Zhu, et al. ∙

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