
Improving Medical Image Classification with Label Noise Using Dualuncertainty Estimation
Deep neural networks are known to be datadriven and label noise can hav...
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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 ...
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Understanding the Interaction of Adversarial Training with Noisy Labels
Noisy labels (NL) and adversarial examples both undermine trained models...
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Provably Endtoend LabelNoise Learning without Anchor Points
In labelnoise learning, the transition matrix plays a key role in build...
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Learning DiverseStructured Networks for Adversarial Robustness
In adversarial training (AT), the main focus has been the objective and ...
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Tackling InstanceDependent Label Noise via a Universal Probabilistic Model
The drastic increase of data quantity often brings the severe decrease o...
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A SecondOrder Approach to Learning with InstanceDependent Label Noise
The presence of label noise often misleads the training of deep neural n...
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Extended T: Learning with Mixed Closedset and Openset Noisy Labels
The label noise transition matrix T, reflecting the probabilities that t...
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A Survey of Labelnoise Representation Learning: Past, Present and Future
Classical machine learning implicitly assumes that labels of the trainin...
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Maximum Mean Discrepancy is Aware of Adversarial Attacks
The maximum mean discrepancy (MMD) test, as a representative twosample ...
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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...
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Quantum differentially private sparse regression learning
Differentially private (DP) learning, which aims to accurately extract p...
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Weakly Supervised Temporal Action Localization with SegmentLevel Labels
Temporal action localization presents a tradeoff between test performan...
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Partsdependent Label Noise: Towards Instancedependent Label Noise
Learning with the instancedependent label noise is challenging, because...
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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...
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Dual T: Reducing Estimation Error for Transition Matrix in Labelnoise Learning
The transition matrix, denoting the transition relationship from clean l...
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Repulsive Mixture Models of Exponential Family PCA for Clustering
The mixture extension of exponential family principal component analysis...
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Quantum noise protects quantum classifiers against adversaries
Noise in quantum information processing is often viewed as a disruptive ...
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MultiClass Classification from NoisySimilarityLabeled Data
A similarity label indicates whether two instances belong to the same cl...
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Towards Mixture Proportion Estimation without Irreducibility
Mixture proportion estimation (MPE) is a fundamental problem of practica...
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Confidence Scores Make Instancedependent Labelnoise Learning Possible
Learning with noisy labels has drawn a lot of attention. In this area, m...
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A Shape Transformationbased Dataset Augmentation Framework for Pedestrian Detection
Deep learningbased computer vision is usually datahungry. Many researc...
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Continuous Dropout
Dropout has been proven to be an effective algorithm for training robust...
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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...
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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 ...
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A Quantuminspired Algorithm for General Minimum Conical Hull Problems
A wide range of fundamental machine learning tasks that are addressed by...
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Truncated Cauchy Nonnegative Matrix Factorization
Nonnegative matrix factorization (NMF) minimizes the Euclidean distance...
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Are Anchor Points Really Indispensable in LabelNoise Learning?
In labelnoise learning, noise transition matrix, denoting the probabili...
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Orthogonal Deep Neural Networks
In this paper, we introduce the algorithms of Orthogonal Deep Neural Net...
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DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs
Due to the high storage and search efficiency, hashing has become preval...
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dipIQ: Blind Image Quality Assessment by LearningtoRank Discriminable Image Pairs
Objective assessment of image quality is fundamentally important in many...
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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...
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Adaptive Morphological Reconstruction for Seeded Image Segmentation
Morphological reconstruction (MR) is often employed by seeded image segm...
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MultiView Matrix Completion for MultiLabel Image Classification
There is growing interest in multilabel image classification due to its...
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DecompositionBased Transfer Distance Metric Learning for Image Classification
Distance metric learning (DML) is a critical factor for image analysis a...
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Fast Supervised Discrete Hashing
Learningbased hashing algorithms are "hot topics" because they can grea...
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Supervised Discrete Hashing with Relaxation
Datadependent hashing has recently attracted attention due to being abl...
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A Regularization Approach for InstanceBased Superset Label Learning
Different from the traditional supervised learning in which each trainin...
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On Better Exploring and Exploiting Task Relationships in MultiTask Learning: Joint Model and Feature Learning
Multitask learning (MTL) aims to learn multiple tasks simultaneously thr...
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GenerativeDiscriminative Complementary Learning
Majority of stateoftheart deep learning methods for vision applicatio...
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Why ResNet Works? Residuals Generalize
Residual connections significantly boost the performance of deep neural ...
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Robust Angular Local Descriptor Learning
In recent years, the learned local descriptors have outperformed handcra...
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An Optimal Transport View on Generalization
We derive upper bounds on the generalization error of learning algorithm...
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The Expressive Power of Parameterized Quantum Circuits
Parameterized quantum circuits (PQCs) have been broadly used as a hybrid...
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Implementable Quantum Classifier for Nonlinear Data
In this Letter, we propose a quantum machine learning scheme for the cla...
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InstanceDependent PU Learning by Bayesian Optimal Relabeling
When learning from positive and unlabelled data, it is a strong assumpti...
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Domain Generalization via Conditional Invariant Representation
Domain generalization aims to apply knowledge gained from multiple label...
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An InformationTheoretic View for Deep Learning
Deep learning has transformed the computer vision, natural language proc...
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On the Rates of Convergence from Surrogate Risk Minimizers to the Bayes Optimal Classifier
We study the rates of convergence from empirical surrogate risk minimize...
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Learning with Biased Complementary Labels
In this paper we study the classification problem in which we have acces...
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Tongliang Liu
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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.