Tongliang Liu


Publications


Preprint

  • Parts-dependent Label Noise: Towards Instance-dependent Label Noise. [PDF]
    X. Xia, T. Liu, B. Han, N. Wang, M. Gong, H. Liu, G. Niu, D. Tao, and M. Sugiyama.

  • Class2Simi: A New Perspective on Learning with Label Noise. [PDF]
    S. Wu, X. Xia, T. Liu, B. Han, M. Gong, N. Wang, H. Liu, and G. Niu.

  • Multi-Class Classification from Noisy-Similarity-Labeled Data. [PDF]
    S. Wu*, X. Xia*, T. Liu, B. Han, M. Gong, N. Wang, H. Liu, and G. Niu.

  • Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. [PDF]
    Y. Yao, T. Liu, B. Han, M. Gong, J. Deng, G. Niu, and M. Sugiyama.

  • Towards mixture proportion estimation without irreducibility. [PDF]
    Y. Yao, T. Liu, B. Han, M. Gong, G. Niu, M. Sugiyama, and D. Tao.

  • Confidence Scores Make Instance-dependent Label-noise Learning Possible. [PDF]
    A. Berthon, B. Han, G. Niu, T. Liu, and M. Sugiyama.

  • Where is the bottleneck of adversarial learning with unlabeled data? [PDF]
    J. Zhang*, B. Han*, G. Niu, T. Liu, and M. Sugiyama.


Conference papers (fully reviewed)

  • Sub-center ArcFace: Boosting Face Recognition by Large-scale Noisy Web Faces.
    J. Deng, J. Guo, T. Liu, M. Gong, and S Zafeiriou.
    In ECCV, 2020.

  • Deep Heterogeneous Multi-Task Metric Learning for Visual Recognition and Retrieval.
    S. Gan, Y. Luo, Y. Wen, T. Liu, and H. Hu.
    In ACM MM, 2020.

  • Learning with Bounded Instance- and Label-dependent Label Noise.
    J. Cheng, T. Liu, K. Rao, and D. Tao.
    In ICML, 2020.

  • Label-Noise Robust Domain Adaptation.
    X. Yu, T. Liu, M. Gong, K. Zhang, K. Batmanghelich, and D. Tao.
    In ICML, 2020.

  • Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks.
    Y. Zhang, Y. Li, T. Liu, and X. Tian.
    In ICML, 2020.

  • LTF: A Label Transformation Framework for Correcting Label Shift.
    J. Guo, M. Gong, T. Liu, K. Zhang, and D. Tao.
    In ICML, 2020.

  • Generative-Discriminative Complementary Learning. [PDF]
    Y. Xu, M. Gong, J. Chen, T. Liu, K. Zhang, and K. Batmanghelich.
    In AAAI, 2020.

  • Diversified Bayesian Nonnegative Matrix Factorization. [PDF]
    M. Qiao, J. Yu, T. Liu, X. Wang, and D. Tao.
    In AAAI, 2020.

  • Are Anchor Points Really Indispensable in Label-Noise Learning? [PDF]
    X. Xia, T. Liu, N. Wang, B. Han, C. Gong, G. Niu, and M. Sugiyama.
    In NeurIPS, 2019.

  • Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence. [PDF]
    F. He, T. Liu, and D. Tao.
    In NeurIPS, 2019.

  • DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs. [PDF]
    E. Yang, T. Liu, C. Deng, W. Liu, and D. Tao.
    In CVPR, 2019.

  • Skipping Two Layers in ResNet Makes the Generalization Gap Smaller than Skipping One or No Layer.
    Y. Furusho, T. Liu, and K. Ikeda.
    In INNSBDDL, 2019.

  • Positive and Unlabeled Learning with Label Disambiguation. [PDF]
    C. Zhang, D. Ren, T. Liu, J. Yang, and C. Gong.
    In IJCAI, 2019.

  • Towards Digital Retina in Smart Cities: A Model Generation, Utilization and Communication Paradigm. [PDF] [Best Paper Award]
    Y. Lou, L. Duan, Y. Luo, Z. Chen, T. Liu, S. Wang, and W. Gao.
    In ICME, 2019.

  • An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption. [PDF]
    X. Yu, T. Liu, M. Gong, K. Batmanghelich, and D. Tao.
    In CVPR, 2018.

  • Learning with Biased Complementary Labels. [PDF]
    X. Yu, T. Liu, M. Gong, and D. Tao.
    In ECCV, 2018.

  • Correcting the Triplet Selection Bias for Triplet Loss. [PDF]
    B. Yu, T. Liu, M. Gong, C. Ding, and D. Tao.
    In ECCV, 2018.

  • Deep Domain Generalization via Conditional Invariant Adversarial Networks. [PDF]
    Y. Li, X. Tian, M. Gong, Y. Liu, T. Liu, K. Zhang, and D. Tao.
    In ECCV, 2018.

  • Quantum Divide-and-Conquer Anchoring for Separable Non-negative Matrix Factorization. [PDF]
    Y. Du, T. Liu, Y. Li, R. Duan, and D. Tao.
    In IJCAI, 2018.

  • Online Heterogeneous Transfer Metric Learning. [PDF]
    Y. Luo, T. Liu, Y. Wen, and D. Tao.
    In IJCAI, 2018.

  • Semantic Structure-based Unsupervised Deep Hashing. [PDF]
    E. Yang, C. Deng, T. Liu, W. Liu, and D. Tao.
    In IJCAI, 2018.

  • Domain Generalization via Conditional Invariant Representations. [PDF]
    Y. Li, M. Gong, X. Tian, T. Liu, and D. Tao.
    In AAAI, 2018

  • Robust Angular Local Descriptor Learning. [PDF]
    Y. Xu, M. Gong, T. Liu, K. Batmanghelich, C. Wang.
    In ACCV, 2018.

  • Algorithmic Stability and Hypothesis Complexity. [PDF]
    T. Liu, G. Lugosi, G. Neu and D. Tao.
    In ICML , 2017.

  • On Compressing Deep Models by Low Rank and Sparse Decomposition. [PDF]
    X. Yu, T. Liu, X. Wang, and D. Tao.
    In CVPR, 2017.

  • Understanding How Feature Structure Transfers in Transfer Learning. [PDF]
    T. Liu, Q. Yang, and D. Tao.
    In IJCAI, 2017.

  • General Heterogeneous Transfer Distance Metric Learning via Knowledge Fragments Transfer. [PDF]
    Y. Luo, Y. Wen, T. Liu, and D. Tao.
    In IJCAI, 2017.

  • Domain Adaptation with Conditional Transferable Components. [PDF]
    M. Gong, K. Zhang, T. Liu, D. Tao, C. Glymour, and B. Schölkopf.
    In ICML, 2106.

  • Diversified Dynamical Gaussian Process Latent Variable Model for Video Repair. [PDF]
    H. Xiong, T. Liu, and D. Tao.
    In AAAI, 2016.

  • Spectral Ensemble Clustering. [PDF]
    H. Liu*, T. Liu*, J. Wu, D. Tao, and Y. Fu.
    In KDD, 2015.

  • Multi-Task Model and Feature Joint Learning. [PDF]
    Y. Li, X. Tian, T. Liu*, and D. Tao.
    In IJCAI, 2015.

  • Learning Relative Features through Adaptive Pooling for Image Classification. [PDF] [Best Paper Award candidate]
    M. Shao, S. Li, T. Liu*, D. Tao, T. S. Huang, and Y. Fu.
    In ICME, 2014.

  • On the Robustness and Generalization of Cauchy Regression. [PDF] [Best Paper Award]
    T. Liu and D. Tao.
    In ICIST, 2014.


Journal articles

  • Orthogonal Deep Neural Networks.
    K. Jia, S. Li, Y. Wen, T. Liu, and D. Tao.
    IEEE T-PAMI, accepted.

  • Loss Decomposition and Centroid Estimation for Positive and Unlabeled Learning.
    C. Gong, H. Shi, T. Liu, C. Zhang, J. Yang, and D. Tao.
    IEEE T-PAMI, accepted.

  • Harnessing Side Information for Classification under Label Noise.
    Y. Wei, C. Gong, S. Chen, T. Liu, J. Yang, and D. Tao.
    IEEE T-NNLS, Accepted.

  • Two-Stream Deep Hashing with Class-Specific Centers for Image Search.
    C. Deng, E. Yang, T. Liu, and D. Tao.
    IEEE T-NNLS, Accepted.

  • Why ResNet Works? Residuals Generalize.
    F. He, T. Liu, and D. Tao.
    IEEE T-NNLS, Accepted.

  • Group Feedback Capsule Network.
    X. Ding, N Wang, X. Gao, J. Li, X. Wang, T. Liu.
    IEEE T-IP, Accepted.

  • Label Propagated Nonnegative Matrix Factorization for Clustering.
    L. Lan, T. Liu, X. Zhang, C. Xu, and Z. Luo.
    IEEE T-KDE, Accepted.

  • Laplacian Welsch Regularization for Robust Semi-Supervised Learning.
    J. Ke, C. Gong, T. Liu, L. Zhao, J. Yang, and D. Tao.
    IEEE T-CYB, Accepted.

  • Towards Efficient Front-end Visual Sensing for Digital Retina: A Model-Centric Paradigm.
    Y. Lou, L. Duan, Y. Luo, Z. Chen, T. Liu, S. Wang, and W. Gao.
    IEEE T-MM, Accepted.

  • Absent Multiple Kernel Learning Algorithms. [PDF]
    X. Liu, L. Wang, X. Zhu, M. Li, E. Zhu, T. Liu, L. Liu, Y. Dou, and J. Yin.
    IEEE T-PAMI, 42(6): 1303-1316, 2020.

  • Multiple Kernel k-means with Incomplete Kernels. [PDF]
    X. Liu, X. Zhu, M. Li, L. Wang, E. Zhu, T. Liu, M. Kloft, D. Shen, J. Yin, and W. Gao.
    IEEE T-PAMI, 42(5): 1191-1204, 2020.

  • Adversarial Examples for Hamming Space Search. [PDF]
    E. Yang, T. Liu, C. Deng, and D. Tao.
    IEEE T-CYB, 50(4): 1473-1484, 2020.

  • Transferring Knowledge Fragments for Learning Distance Metric from A Heterogeneous Domain. [PDF]
    Y. Luo, Y. Wen, T. Liu, and D. Tao.
    IEEE T-PAMI, 41(4): 1013-1026, 2019.

  • Truncated Cauchy Non-negative Matrix Factorization. [PDF]
    N. Guan*, T. Liu*, Y. Zhang, D. Tao, and L. S. Davis.
    IEEE T-PAMI, 41(1): 246-259, 2019.

  • Large-Margin Label-Calibrated Support Vector Machines for Positive and Unlabeled Learning. [PDF]
    C. Gong, T. Liu, J. Yang, and D. Tao.
    IEEE T-NNLS, 30(11): 3471-3483, 2019.

  • Eigenfunction-Based Multitask Learning in a Reproducing Kernel Hilbert Space. [PDF]
    X. Tian, Y. Li, T. Liu, X. Wang, and D. Tao.
    IEEE T-NNLS, 30(6): 1818-1830, 2019.

  • Adaptive Morphological Reconstruction for Seeded Image Segmentation. [PDF]
    T. Lei, X. Jia, T. Liu, S. Liu, H. Meng, and A. K. Nandi.
    IEEE T-IP, 28(11): 5510-5523, 2019.

  • Unsupervised Semantic-Preserving Adversarial Hashing for Image Search. [PDF]
    C. Deng, E. Yang, T. Liu, J. Li, W. Liu, and D. Tao.
    IEEE T-IP, 28(8): 4032-4044, 2019.

  • Fast Supervised Discrete Hashing. [PDF]
    J. Gui*, T. Liu*, Z. Sun, D. Tao, and T. Tan.
    IEEE T-PAMI, 40(2): 490-496, 2018.

  • Continuous Dropout. [PDF]
    X. Shen, X. Tian, F. Xu, T. Liu, and D. Tao.
    IEEE T-NNLS, 29(9): 3926-3937, 2018.

  • Multi-class Learning with Partially Corrupted Labels. [PDF]
    R. Wang, T. Liu, and D. Tao.
    IEEE T-NNLS, 29(6): 2568-2580, 2018.

  • On Better Exploring and Exploiting Task Relationship in Multi-Task Learning: Joint Model and Feature Learning. [PDF]
    Y. Li, X. Tian, T. Liu, and D. Tao.
    IEEE T-NNLS, 29(5): 1975-1985, 2018.

  • Supervised Discrete Hashing with Relaxation. [PDF]
    J. Gui*, T. Liu*, Z. Sun, D. Tao, and T. Tan.
    IEEE T-NNLS, 29(3): 608-617, 2018.

  • Deep Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks. [PDF]
    K. Ma, H. Fu, T. Liu, Z. Wang, and D. Tao.
    IEEE T-IP, 27(10): 5155-5166, 2018.

  • A Regularization Approach for Instance-Based Superset Label Learning. [PDF]
    C. Gong, T. Liu, Y. Tang, J. Yang, J. Yang, and D. Tao.
    IEEE T-CYB, 48(3): 967-978, 2018.

  • Algorithm-Dependent Generalization Bounds for Multi-Task Learning. [PDF]
    T. Liu, D. Tao, M. Song, and S. J. Maybank.
    IEEE T-PAMI, 39(2): 227-241, 2017.

  • Large Cone Non-negative Matrix Factorization. [PDF]
    T. Liu, M. Gong, and D. Tao.
    IEEE T-NNLS, 28(9): 2129-2141, 2017.

  • dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs. [PDF]
    K. Ma, W. Liu, T. Liu, Z. Wang, and D. Tao.
    IEEE T-IP, 26(8): 3951-3964, 2017.

  • Elastic Net Hypergraph Learning for Image Clustering and Semi-supervised Classification. [PDF]
    Q. Liu, Y. Sun, C. Wang, T. Liu, and D. Tao.
    IEEE T-IP, 26(1): 452-463, 2017.

  • Spectral Ensemble Clustering via Weighted K-means: Theoretical and Practical Evidence. [PDF]
    H. Liu, J. Wu, T. Liu, D. Tao, and Y. Fu.
    IEEE T-KDE, vol. 29, no. 5, pp. 1129-1143, 2017.

  • Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection. [PDF]
    Y. Zhang, B. Du, L. Zhang, and T. Liu.
    IEEE T-GRS, 55(2): 894-906, 2017.

  • Classification with Noisy Labels by Importance Reweighting. [PDF]
    T. Liu and D. Tao.
    IEEE T-PAMI, 38(3): 447-461, 2016.

  • Dimensionality-Dependent Generalization Bounds for k-Dimensional Coding Schemes. [PDF]
    T. Liu, D. Tao, and D. Xu.
    NECO, 28(10): 2213-2249, 2016.

  • On the Performance of MahNMF Manhattan Non-negative Matrix Factorization. [PDF]
    T. Liu and D. Tao.
    IEEE T-NNLS, 27(9): 1851-1863, 2016.

  • Dual Diversified Dynamical Gaussian Process Latent Variable Model for Video Repair. [PDF]
    H. Xiong, T. Liu, D. Tao, and H. T. Shen.
    IEEE T-IP, 25(8): 3626-3637, 2016.

  • Local Rademacher Complexity for Multi-Label Learning. [PDF]
    C. Xu, T. Liu, D. Tao, and C. Xu.
    IEEE T-IP, 25(3): 1495-1507, 2016.

  • Representative Vector Machines: A Unified Framework for Classical Classifiers. [PDF]
    J. Gui*, T. Liu*, D. Tao, Z. Sun, and T. Tan.
    IEEE T-CYB, 46(8): 1877-1888, 2016.

  • Video Face Editing Using Temporal-Spatial-Smooth Warping. [PDF]
    X. Li, T. Liu*, J. Deng, and D. Tao.
    ACM T-IST, 7(3): 32, 2016.

  • Deformed Graph Laplacian for Semisupervised Learning. [PDF]
    C. Gong, T. Liu, D. Tao, K. Fu, E. Tu, and J. Yang.
    IEEE T-NNLS, 26(10): 2261-2274, 2015.

  • Multiview Matrix Completion for Multilabel Image Classification. [PDF]
    Y. Luo, T. Liu, D. Tao, and C. Xu.
    IEEE T-IP, 24(8): 2261-2274, 2015.

  • No Reference Quality Assessment for Multiply-Distorted Images Based on an Improved Bag-of-Words Model. [PDF]
    Y. Lu, F. Xie, T. Liu, Z. Jiang, and D. Tao.
    IEEE SPL, 22(10): 1811-1815, 2015.

  • Decomposition-Based Transfer Distance Metric Learning for Image Classification. [PDF]
    Y. Luo, T. Liu, D. Tao, and C. Xu.
    IEEE T-IP, 23(9): 3789-3801, 2014.