Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDTongliang Liu
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2023
[j66]Xiaobo Xia
, Bo Han, Nannan Wang, Jiankang Deng, Jiatong Li, Yinian Mao, Tongliang Liu:
Extended $T$T: Learning With Mixed Closed-Set and Open-Set Noisy Labels. IEEE Trans. Pattern Anal. Mach. Intell. 45(3): 3047-3058 (2023)- [j65]Shenghong He, Ruxin Wang, Tongliang Liu, Chao Yi, Xin Jin, Renyang Liu, Wei Zhou:
Type-I Generative Adversarial Attack. IEEE Trans. Dependable Secur. Comput. 20(3): 2593-2606 (2023) - [j64]Erdun Gao, Junjia Chen, Li Shen, Tongliang Liu, Mingming Gong, Howard D. Bondell:
FedDAG: Federated DAG Structure Learning. Trans. Mach. Learn. Res. 2023 (2023) - [j63]Chenhong Zhou, Feng Liu, Chen Gong, Rongfei Zeng, Tongliang Liu, Kwok-Wai Cheung, Bo Han:
KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation. Trans. Mach. Learn. Res. 2023 (2023) - [j62]Shikun Li, Tongliang Liu, Jiyong Tan, Dan Zeng, Shiming Ge:
Trustable Co-Label Learning From Multiple Noisy Annotators. IEEE Trans. Multim. 25: 1045-1057 (2023) - [j61]Jie Ma, Jun Liu, Yaxian Wang, Junjun Li, Tongliang Liu:
Relation-Aware Fine-Grained Reasoning Network for Textbook Question Answering. IEEE Trans. Neural Networks Learn. Syst. 34(1): 15-27 (2023) - [i127]Ling-Hao Chen, Jiawei Zhang, Yewen Li, Yiren Pang, Xiaobo Xia, Tongliang Liu:
HumanMAC: Masked Motion Completion for Human Motion Prediction. CoRR abs/2302.03665 (2023) - [i126]Jianing Zhu, Jiangchao Yao, Tongliang Liu, Quanming Yao, Jianliang Xu, Bo Han:
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning. CoRR abs/2303.00250 (2023) - [i125]Jiren Mai, Fei Zhang, Junjie Ye, Marcus Kalander, Xian Zhang, Wankou Yang, Tongliang Liu, Bo Han:
Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation. CoRR abs/2303.02449 (2023) - [i124]Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, Bo Han:
Out-of-distribution Detection with Implicit Outlier Transformation. CoRR abs/2303.05033 (2023) - [i123]Zixuan Hu, Li Shen, Zhenyi Wang, Tongliang Liu, Chun Yuan, Dacheng Tao:
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning. CoRR abs/2303.11183 (2023) - [i122]Xiu-Chuan Li, Xiaobo Xia, Fei Zhu, Tongliang Liu, Xu-Yao Zhang, Cheng-Lin Liu:
Dynamics-Aware Loss for Learning with Label Noise. CoRR abs/2303.11562 (2023) - [i121]Jiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu, Sijia Liu, Masashi Sugiyama, Yang Liu:
Fairness Improves Learning from Noisily Labeled Long-Tailed Data. CoRR abs/2303.12291 (2023) - [i120]Shuo Yang, Zhaopan Xu, Kai Wang, Yang You, Hongxun Yao, Tongliang Liu, Min Xu:
BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency. CoRR abs/2303.12419 (2023) - [i119]Zhuo Huang, Miaoxi Zhu, Xiaobo Xia, Li Shen, Jun Yu, Chen Gong, Bo Han, Bo Du, Tongliang Liu:
Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization. CoRR abs/2303.13087 (2023) - [i118]Xiang An, Jiankang Deng, Kaicheng Yang, Jaiwei Li, Ziyong Feng, Jia Guo, Jing Yang, Tongliang Liu:
Unicom: Universal and Compact Representation Learning for Image Retrieval. CoRR abs/2304.05884 (2023) - [i117]Dongting Hu, Zhenkai Zhang, Tingbo Hou, Tongliang Liu, Huan Fu, Mingming Gong:
Multiscale Representation for Real-Time Anti-Aliasing Neural Rendering. CoRR abs/2304.10075 (2023) - [i116]Yulong Yang, Chenhao Lin, Qian Li, Chao Shen, Dawei Zhou, Nannan Wang, Tongliang Liu:
Quantization Aware Attack: Enhancing the Transferability of Adversarial Attacks across Target Models with Different Quantization Bitwidths. CoRR abs/2305.05875 (2023) - [i115]Bochao Liu, Shiming Ge, Pengju Wang, Liansheng Zhuang, Tongliang Liu:
Learning Differentially Private Probabilistic Models for Privacy-Preserving Image Generation. CoRR abs/2305.10662 (2023) - 2022
- [j60]Shijun Cai, Seok-Hee Hong, Jialiang Shen, Tongliang Liu:
A Machine Learning Approach for Predicting Human Preference for Graph Layouts. J. Graph Algorithms Appl. 26(1): 447-471 (2022) - [j59]Xu Yang, Cheng Deng, Tongliang Liu, Dacheng Tao:
Heterogeneous Graph Attention Network for Unsupervised Multiple-Target Domain Adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 44(4): 1992-2003 (2022) - [j58]Chen Gong, Qizhou Wang, Tongliang Liu, Bo Han, Jane You, Jian Yang, Dacheng Tao:
Instance-Dependent Positive and Unlabeled Learning With Labeling Bias Estimation. IEEE Trans. Pattern Anal. Mach. Intell. 44(8): 4163-4177 (2022) - [j57]Hao Wang, Cheng Deng, Tongliang Liu, Dacheng Tao:
Transferable Coupled Network for Zero-Shot Sketch-Based Image Retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 9181-9194 (2022) - [j56]Shuo Yang, Songhua Wu, Tongliang Liu, Min Xu:
Bridging the Gap Between Few-Shot and Many-Shot Learning via Distribution Calibration. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 9830-9843 (2022) - [j55]Guoqing Bao, Huai Chen, Tongliang Liu, Guanzhong Gong, Yong Yin, Lisheng Wang, Xiuying Wang:
COVID-MTL: Multitask learning with Shift3D and random-weighted loss for COVID-19 diagnosis and severity assessment. Pattern Recognit. 124: 108499 (2022) - [j54]Jingchen Ke, Chen Gong, Tongliang Liu, Lin Zhao, Jian Yang, Dacheng Tao:
Laplacian Welsch Regularization for Robust Semisupervised Learning. IEEE Trans. Cybern. 52(1): 164-177 (2022) - [j53]Xinpeng Ding, Nannan Wang, Shiwei Zhang, Ziyuan Huang, Xiaomeng Li, Mingqian Tang, Tongliang Liu, Xinbo Gao:
Exploring Language Hierarchy for Video Grounding. IEEE Trans. Image Process. 31: 4693-4706 (2022) - [j52]Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Shan You, Dacheng Tao:
Quantum Differentially Private Sparse Regression Learning. IEEE Trans. Inf. Theory 68(8): 5217-5233 (2022) - [j51]Long Lan, Tongliang Liu, Xiang Zhang, Chuanfu Xu, Zhigang Luo:
Label Propagated Nonnegative Matrix Factorization for Clustering. IEEE Trans. Knowl. Data Eng. 34(1): 340-351 (2022) - [j50]Lie Ju, Xin Wang, Lin Wang, Dwarikanath Mahapatra, Xin Zhao, Quan Zhou, Tongliang Liu, Zongyuan Ge:
Improving Medical Images Classification With Label Noise Using Dual-Uncertainty Estimation. IEEE Trans. Medical Imaging 41(6): 1533-1546 (2022) - [j49]Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Lizhen Cui, Gang Niu, Masashi Sugiyama:
NoiLin: Improving adversarial training and correcting stereotype of noisy labels. Trans. Mach. Learn. Res. 2022 (2022) - [j48]Zhaoyu Zhang, Mengyan Li, Haonian Xie, Jun Yu, Tongliang Liu, Chang Wen Chen:
TWGAN: Twin Discriminator Generative Adversarial Networks. IEEE Trans. Multim. 24: 677-688 (2022) - [j47]Zhengning Wu, Xiaobo Xia, Ruxin Wang, Jiatong Li, Jun Yu, Yinian Mao, Tongliang Liu:
LR-SVM+: Learning Using Privileged Information with Noisy Labels. IEEE Trans. Multim. 24: 1080-1092 (2022) - [j46]Jingwei Zhang, Tongliang Liu, Dacheng Tao:
On the Rates of Convergence From Surrogate Risk Minimizers to the Bayes Optimal Classifier. IEEE Trans. Neural Networks Learn. Syst. 33(10): 5766-5774 (2022) - [j45]Shijun Cai, Seok-Hee Hong, Xiaobo Xia, Tongliang Liu, Weidong Huang:
A machine learning approach for predicting human shortest path task performance. Vis. Informatics 6(2): 50-61 (2022) - [c101]Amirmohammad Pasdar, Young Choon Lee, Tongliang Liu, Seok-Hee Hong:
Train Me to Fight: Machine-Learning Based On-Device Malware Detection for Mobile Devices. CCGRID 2022: 239-248 - [c100]Masashi Sugiyama, Tongliang Liu, Bo Han, Yang Liu, Gang Niu:
Learning and Mining with Noisy Labels. CIKM 2022: 5152-5155 - [c99]Songhua Wu, Mingming Gong, Bo Han, Yang Liu, Tongliang Liu:
Fair Classification with Instance-dependent Label Noise. CLeaR 2022: 927-943 - [c98]Shikun Li, Xiaobo Xia, Shiming Ge, Tongliang Liu:
Selective-Supervised Contrastive Learning with Noisy Labels. CVPR 2022: 316-325 - [c97]Xiang An, Jiankang Deng, Jia Guo, Ziyong Feng, Xuhan Zhu, Jing Yang, Tongliang Liu:
Killing Two Birds with One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC. CVPR 2022: 4032-4041 - [c96]Xiaoqing Guo, Jie Liu, Tongliang Liu, Yixuan Yuan:
SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation. CVPR 2022: 7022-7031 - [c95]Erkun Yang, Dongren Yao, Tongliang Liu, Cheng Deng:
Mutual Quantization for Cross-Modal Search with Noisy Labels. CVPR 2022: 7541-7550 - [c94]Zhaoqing Wang, Yu Lu, Qiang Li, Xunqiang Tao, Yandong Guo, Mingming Gong, Tongliang Liu:
CRIS: CLIP-Driven Referring Image Segmentation. CVPR 2022: 11676-11685 - [c93]Zhaoqing Wang, Qiang Li, Guoxin Zhang, Pengfei Wan, Wen Zheng, Nannan Wang, Mingming Gong, Tongliang Liu:
Exploring Set Similarity for Dense Self-supervised Representation Learning. CVPR 2022: 16569-16578 - [c92]De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama:
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. CVPR 2022: 16609-16618 - [c91]Xin Jin, Tianyu He, Xu Shen, Songhua Wu, Tongliang Liu, Jingwen Ye, Xinchao Wang, Jianqiang Huang, Zhibo Chen, Xian-Sheng Hua:
Unleashing the Potential of Adaptation Models via Go-getting Domain Labels. ECCV Workshops (8) 2022: 308-325 - [c90]Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng:
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. ICLR 2022 - [c89]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. ICLR 2022 - [c88]Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, Yang Liu:
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations. ICLR 2022 - [c87]Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama:
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. ICLR 2022 - [c86]Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao:
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning. ICLR 2022 - [c85]Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama:
Exploiting Class Activation Value for Partial-Label Learning. ICLR 2022 - [c84]Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang:
Adversarial Robustness Through the Lens of Causality. ICLR 2022 - [c83]Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, Hongxia Yang:
Reliable Adversarial Distillation with Unreliable Teachers. ICLR 2022 - [c82]Joshua Y. Kim, Tongliang Liu, Kalina Yacef:
Improving Supervised Learning in Conversational Analysis through Reusing Preprocessing Data as Auxiliary Supervisors. ICMI Companion 2022: 134-143 - [c81]Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu:
To Smooth or Not? When Label Smoothing Meets Noisy Labels. ICML 2022: 23589-23614 - [c80]Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu:
Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network. ICML 2022: 25302-25312 - [c79]Chaojian Yu, Bo Han, Li Shen, Jun Yu, Chen Gong, Mingming Gong, Tongliang Liu:
Understanding Robust Overfitting of Adversarial Training and Beyond. ICML 2022: 25595-25610 - [c78]Dawei Zhou, Nannan Wang, Xinbo Gao, Bo Han, Xiaoyu Wang, Yibing Zhan, Tongliang Liu:
Improving Adversarial Robustness via Mutual Information Estimation. ICML 2022: 27338-27352 - [c77]Dawei Zhou, Nannan Wang, Bo Han, Tongliang Liu:
Modeling Adversarial Noise for Adversarial Training. ICML 2022: 27353-27366 - [c76]Chaojian Yu, Bo Han, Mingming Gong, Li Shen, Shiming Ge, Du Bo, Tongliang Liu:
Robust Weight Perturbation for Adversarial Training. IJCAI 2022: 3688-3694 - [c75]Xiong Peng, Feng Liu, Jingfeng Zhang, Long Lan, Junjie Ye, Tongliang Liu, Bo Han:
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks. KDD 2022: 1358-1367 - [c74]Xiaobo Xia, Shuo Shan, Mingming Gong, Nannan Wang, Fei Gao, Haikun Wei, Tongliang Liu:
Sample-Efficient Kernel Mean Estimator with Marginalized Corrupted Data. KDD 2022: 2110-2119 - [c73]Xin Jin, Tianyu He, Xu Shen, Tongliang Liu, Xinchao Wang, Jianqiang Huang, Zhibo Chen, Xian-Sheng Hua:
Meta Clustering Learning for Large-scale Unsupervised Person Re-identification. ACM Multimedia 2022: 2163-2172 - [c72]Yong Luo, Ling-Yu Duan, Yan Bai, Tongliang Liu, Yihang Lou, Yonggang Wen:
Nonlinear Multi-Model Reuse. MMSP 2022: 1-6 - [c71]Amirmohammad Pasdar, Young Choon Lee, Seok-Hee Hong, Tongliang Liu:
MAPS: a dataset for semantic profiling and analysis of Android applications. MobiArch@MobiCom 2022: 13-18 - [c70]Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng:
Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs. NeurIPS 2022 - [c69]Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama:
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. NeurIPS 2022 - [c68]Yingbin Bai, Erkun Yang, Zhaoqing Wang, Yuxuan Du, Bo Han, Cheng Deng, Dadong Wang, Tongliang Liu:
RSA: Reducing Semantic Shift from Aggressive Augmentations for Self-supervised Learning. NeurIPS 2022 - [c67]De Cheng, Yixiong Ning, Nannan Wang, Xinbo Gao, Heng Yang, Yuxuan Du, Bo Han, Tongliang Liu:
Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization. NeurIPS 2022 - [c66]Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard D. Bondell:
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. NeurIPS 2022 - [c65]Yewen Li, Chaojie Wang, Xiaobo Xia, Tongliang Liu, Xin Miao, Bo An:
Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE. NeurIPS 2022 - [c64]Shikun Li, Xiaobo Xia, Hansong Zhang, Yibing Zhan, Shiming Ge, Tongliang Liu:
Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning. NeurIPS 2022 - [c63]Chenghao Sun, Yonggang Zhang, Chaoqun Wan, Qizhou Wang, Ya Li, Tongliang Liu, Bo Han, Xinmei Tian:
Towards Lightweight Black-Box Attack Against Deep Neural Networks. NeurIPS 2022 - [c62]Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, Bo Han:
Watermarking for Out-of-distribution Detection. NeurIPS 2022 - [c61]Xiaobo Xia, Wenhao Yang, Jie Ren, Yewen Li, Yibing Zhan, Bo Han, Tongliang Liu:
Pluralistic Image Completion with Gaussian Mixture Models. NeurIPS 2022 - [c60]Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong:
Counterfactual Fairness with Partially Known Causal Graph. NeurIPS 2022 - [i114]Yexiong Lin, Yu Yao, Yuxuan Du, Jun Yu, Bo Han, Mingming Gong, Tongliang Liu:
Do We Need to Penalize Variance of Losses for Learning with Label Noise? CoRR abs/2201.12739 (2022) - [i113]Yongqiang Chen, Yonggang Zhang, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng:
Invariance Principle Meets Out-of-Distribution Generalization on Graphs. CoRR abs/2202.05441 (2022) - [i112]Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng:
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. CoRR abs/2202.08057 (2022) - [i111]Shikun Li, Xiaobo Xia, Shiming Ge, Tongliang Liu:
Selective-Supervised Contrastive Learning with Noisy Labels. CoRR abs/2203.04181 (2022) - [i110]Shikun Li, Tongliang Liu, Jiyong Tan, Dan Zeng, Shiming Ge:
Trustable Co-label Learning from Multiple Noisy Annotators. CoRR abs/2203.04199 (2022) - [i109]Xiaoqing Guo, Jie Liu, Tongliang Liu, Yixuan Yuan:
SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation. CoRR abs/2203.15202 (2022) - [i108]Xiang An, Jiankang Deng, Jia Guo, Ziyong Feng, Xuhan Zhu, Jing Yang, Tongliang Liu:
Killing Two Birds with One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC. CoRR abs/2203.15565 (2022) - [i107]Chuang Liu, Yibing Zhan, Chang Li, Bo Du, Jia Wu, Wenbin Hu, Tongliang Liu, Dacheng Tao:
Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities. CoRR abs/2204.07321 (2022) - [i106]Xiaobo Xia, Wenhao Yang, Jie Ren, Yewen Li, Yibing Zhan, Bo Han, Tongliang Liu:
Pluralistic Image Completion with Probabilistic Mixture-of-Experts. CoRR abs/2205.09086 (2022) - [i105]Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard D. Bondell:
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. CoRR abs/2205.13869 (2022) - [i104]Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong:
Counterfactual Fairness with Partially Known Causal Graph. CoRR abs/2205.13972 (2022) - [i103]Chaojian Yu, Bo Han, Mingming Gong, Li Shen, Shiming Ge, Bo Du, Tongliang Liu:
Robust Weight Perturbation for Adversarial Training. CoRR abs/2205.14826 (2022) - [i102]Yingbin Bai, Erkun Yang, Zhaoqing Wang, Yuxuan Du, Bo Han, Cheng Deng, Dadong Wang, Tongliang Liu:
MSR: Making Self-supervised learning Robust to Aggressive Augmentations. CoRR abs/2206.01999 (2022) - [i101]De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama:
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. CoRR abs/2206.02791 (2022) - [i100]Jinkai Tian, Xiaoyu Sun, Yuxuan Du, Shanshan Zhao, Qing Liu, Kaining Zhang, Wei Yi, Wanrong Huang, Chaoyue Wang, Xingyao Wu, Min-Hsiu Hsieh, Tongliang Liu, Wenjing Yang, Dacheng Tao:
Recent Advances for Quantum Neural Networks in Generative Learning. CoRR abs/2206.03066 (2022) - [i99]Xiong Peng, Feng Liu, Jingfen Zhang, Long Lan, Junjie Ye, Tongliang Liu, Bo Han:
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks. CoRR abs/2206.05483 (2022) - [i98]Lianyang Ma, Yu Yao, Tao Liang, Tongliang Liu:
Multi-scale Cooperative Multimodal Transformers for Multimodal Sentiment Analysis in Videos. CoRR abs/2206.07981 (2022) - [i97]Chaojian Yu, Bo Han, Li Shen, Jun Yu, Chen Gong, Mingming Gong, Tongliang Liu:
Understanding Robust Overfitting of Adversarial Training and Beyond. CoRR abs/2206.08675 (2022) - [i96]Zhuo Huang, Xiaobo Xia, Li Shen, Bo Han, Mingming Gong, Chen Gong, Tongliang Liu:
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style. CoRR abs/2207.03162 (2022) - [i95]Dawei Zhou, Nannan Wang, Xinbo Gao, Bo Han, Xiaoyu Wang, Yibing Zhan, Tongliang Liu:
Improving Adversarial Robustness via Mutual Information Estimation. CoRR abs/2207.12203 (2022) - [i94]Xinbiao Wang, Junyu Liu, Tongliang Liu, Yong Luo, Yuxuan Du, Dacheng Tao:
Symmetric Pruning in Quantum Neural Networks. CoRR abs/2208.14057 (2022) - [i93]Chenghao Sun, Yonggang Zhang, Chaoqun Wan, Qizhou Wang, Ya Li, Tongliang Liu, Bo Han, Xinmei Tian:
Towards Lightweight Black-Box Attacks against Deep Neural Networks. CoRR abs/2209.14826 (2022) - [i92]Chaojian Yu, Dawei Zhou, Li Shen, Jun Yu, Bo Han, Mingming Gong, Nannan Wang, Tongliang Liu:
Strength-Adaptive Adversarial Training. CoRR abs/2210.01288 (2022) - [i91]Yuanyuan Wang, Wei Huang, Mingming Gong, Xi Geng, Tongliang Liu, Kun Zhang, Dacheng Tao:
Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations. CoRR abs/2210.05955 (2022) - [i90]Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, Bo Han:
Watermarking for Out-of-distribution Detection. CoRR abs/2210.15198 (2022) - [i89]Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama:
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. CoRR abs/2211.00269 (2022) - [i88]Maoyuan Ye, Jing Zhang, Shanshan Zhao, Juhua Liu, Tongliang Liu, Bo Du, Dacheng Tao:
DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting. CoRR abs/2211.10772 (2022) - [i87]Huaxi Huang, Hui Kang, Sheng Liu, Olivier Salvado, Thierry Rakotoarivelo, Dadong Wang, Tongliang Liu:
PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels. CoRR abs/2212.03462 (2022) - 2021
- [j44]Zhe Chen, Wanli Ouyang, Tongliang Liu, Dacheng Tao:
A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection. Int. J. Comput. Vis. 129(4): 1121-1138 (2021) - [j43]Chen Gong, Hong Shi, Tongliang Liu, Chuang Zhang, Jian Yang, Dacheng Tao:
Loss Decomposition and Centroid Estimation for Positive and Unlabeled Learning. IEEE Trans. Pattern Anal. Mach. Intell. 43(3): 918-932 (2021) - [j42]Shuai Li, Kui Jia, Yuxin Wen, Tongliang Liu, Dacheng Tao:
Orthogonal Deep Neural Networks. IEEE Trans. Pattern Anal. Mach. Intell. 43(4): 1352-1368 (2021) - [j41]Jia Shao, Bo Du, Chen Wu, Mingming Gong, Tongliang Liu:
HRSiam: High-Resolution Siamese Network, Towards Space-Borne Satellite Video Tracking. IEEE Trans. Image Process. 30: 3056-3068 (2021) - [j40]Xinpeng Ding, Nannan Wang, Xinbo Gao, Jie Li, Xiaoyu Wang, Tongliang Liu:
KFC: An Efficient Framework for Semi-Supervised Temporal Action Localization. IEEE Trans. Image Process. 30: 6869-6878 (2021) - [c59]