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2020 – today
- 2025
[j94]Zhuo Huang, Muyang Li, Li Shen, Jun Yu, Chen Gong, Bo Han, Tongliang Liu:
Winning Prize Comes from Losing Tickets: Improve Invariant Learning by Exploring Variant Parameters for Out-of-Distribution Generalization. Int. J. Comput. Vis. 133(1): 456-474 (2025)- [j93]Wenshui Luo
, Shuo Chen, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama, Dacheng Tao, Chen Gong:
Estimating Per-Class Statistics for Label Noise Learning. IEEE Trans. Pattern Anal. Mach. Intell. 47(1): 305-322 (2025) - 2024
- [j92]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. J. Mach. Learn. Res. 25: 154:1-154:50 (2024) - [j91]Jialiang Shen, Yu Yao, Shaoli Huang, Zhiyong Wang, Jing Zhang, Ruxing Wang, Jun Yu, Tongliang Liu:
ProtoSimi: label correction for fine-grained visual categorization. Mach. Learn. 113(4): 1903-1920 (2024) - [j90]DeLiang Wang, Mauro Forti, Tongliang Liu, Taro Toyoizumi:
Expansion of the editorial team. Neural Networks 173: 106209 (2024) - [j89]Sichao Fu, Xueqi Ma, Yibing Zhan, Fanyu You, Qinmu Peng, Tongliang Liu, James Bailey, Danilo P. Mandic:
Finding core labels for maximizing generalization of graph neural networks. Neural Networks 180: 106635 (2024) - [j88]Xiaobo Xia, Pengqian Lu, Chen Gong, Bo Han, Jun Yu, Jun Yu, Tongliang Liu:
Regularly Truncated M-Estimators for Learning With Noisy Labels. IEEE Trans. Pattern Anal. Mach. Intell. 46(5): 3522-3536 (2024) - [j87]Jingfeng Zhang, Bo Song, Haohan Wang, Bo Han, Tongliang Liu, Lei Liu, Masashi Sugiyama:
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-Noise Learning. IEEE Trans. Pattern Anal. Mach. Intell. 46(6): 4398-4409 (2024) - [j86]Songhua Wu, Tianyi Zhou, Yuxuan Du, Jun Yu, Bo Han, Tongliang Liu:
A Time-Consistency Curriculum for Learning From Instance-Dependent Noisy Labels. IEEE Trans. Pattern Anal. Mach. Intell. 46(7): 4830-4842 (2024) - [j85]Jingyi Wang, Xiaobo Xia, Long Lan, Xinghao Wu, Jun Yu, Wenjing Yang, Bo Han, Tongliang Liu:
Tackling Noisy Labels With Network Parameter Additive Decomposition. IEEE Trans. Pattern Anal. Mach. Intell. 46(9): 6341-6354 (2024) - [j84]Shikun Li, Xiaobo Xia, Jiankang Deng, Shiming Ge, Tongliang Liu:
Transferring Annotator- and Instance-Dependent Transition Matrix for Learning From Crowds. IEEE Trans. Pattern Anal. Mach. Intell. 46(11): 7377-7391 (2024) - [j83]Enneng Yang, Zhenyi Wang, Li Shen, Nan Yin, Tongliang Liu, Guibing Guo, Xingwei Wang, Dacheng Tao:
Continual Learning From a Stream of APIs. IEEE Trans. Pattern Anal. Mach. Intell. 46(12): 11432-11445 (2024) - [j82]Yulong Yang, Chenhao Lin, Qian Li, Zhengyu Zhao, Haoran Fan, Dawei Zhou, Nannan Wang, Tongliang Liu, Chao Shen:
Quantization Aware Attack: Enhancing Transferable Adversarial Attacks by Model Quantization. IEEE Trans. Inf. Forensics Secur. 19: 3265-3278 (2024) - [j81]Wankou Yang, Jiren Mai, Fei Zhang, Tongliang Liu, Bo Han:
Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation. Trans. Mach. Learn. Res. 2024 (2024) - [j80]Zhengning Wu, Tianyu He, Xiaobo Xia, Jun Yu, Xu Shen, Tongliang Liu:
Conditional Consistency Regularization for Semi-Supervised Multi-Label Image Classification. IEEE Trans. Multim. 26: 4206-4216 (2024) - [j79]Mingyu Li, Tao Zhou, Bo Han, Tongliang Liu, Xinkai Liang, Jiajia Zhao, Chen Gong:
Class-Wise Contrastive Prototype Learning for Semi-Supervised Classification Under Intersectional Class Mismatch. IEEE Trans. Multim. 26: 8145-8156 (2024) - [j78]Liangchen Liu, Nannan Wang, Decheng Liu, Xi Yang, Xinbo Gao, Tongliang Liu:
Towards Specific Domain Prompt Learning via Improved Text Label Optimization. IEEE Trans. Multim. 26: 10805-10815 (2024) - [j77]Jingwei Zhang, Tongliang Liu, Dacheng Tao:
Going Deeper, Generalizing Better: An Information-Theoretic View for Deep Learning. IEEE Trans. Neural Networks Learn. Syst. 35(11): 16683-16695 (2024) - [j76]Tianfu Wang, Li Shen, Qilin Fan, Tong Xu, Tongliang Liu, Hui Xiong:
Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning. IEEE Trans. Serv. Comput. 17(3): 1001-1015 (2024) - [c177]Qiang Qu, Yiran Shen, Xiaoming Chen, Yuk Ying Chung, Tongliang Liu:
E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning. AAAI 2024: 4632-4640 - [c176]Rundong He, Yue Yuan, Zhongyi Han, Fan Wang, Wan Su, Yilong Yin, Tongliang Liu, Yongshun Gong:
Exploring Channel-Aware Typical Features for Out-of-Distribution Detection. AAAI 2024: 12402-12410 - [c175]Yunshui Li, Binyuan Hui, Xiaobo Xia, Jiaxi Yang, Min Yang, Lei Zhang, Shuzheng Si, Ling-Hao Chen, Junhao Liu, Tongliang Liu, Fei Huang, Yongbin Li:
One-Shot Learning as Instruction Data Prospector for Large Language Models. ACL (1) 2024: 4586-4601 - [c174]Lianyang Ma, Yu Yao, Tao Liang, Tongliang Liu:
Multi-scale Cooperative Multimodal Transformers for Multimodal Sentiment Analysis in Videos. AI (2) 2024: 281-297 - [c173]Ling-Hao Chen, Yuanshuo Zhang, Taohua Huang, Liangcai Su, Zeyi Lin, Xi Xiao, Xiaobo Xia, Tongliang Liu:
ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance. CIKM 2024: 270-280 - [c172]Wei Zhang, Chaoqun Wan, Tongliang Liu, Xinmei Tian, Xu Shen, Jieping Ye:
Enhanced Motion-Text Alignment for Image-to-Video Transfer Learning. CVPR 2024: 18504-18515 - [c171]Ziming Hong, Li Shen, Tongliang Liu:
Your Transferability Barrier is Fragile: Free-Lunch for Transferring the Non-Transferable Learning. CVPR 2024: 28805-28815 - [c170]Zhenyi Wang, Li Shen, Junfeng Guo, Tiehang Duan, Siyu Luan, Tongliang Liu, Mingchen Gao:
Training A Secure Model Against Data-Free Model Extraction. ECCV (79) 2024: 323-340 - [c169]Rong Dai, Yonggang Zhang, Ang Li, Tongliang Liu, Xun Yang, Bo Han:
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting. ICLR 2024 - [c168]Ziming Hong, Zhenyi Wang, Li Shen, Yu Yao, Zhuo Huang, Shiming Chen, Chuanwu Yang, Mingming Gong, Tongliang Liu:
Improving Non-Transferable Representation Learning by Harnessing Content and Style. ICLR 2024 - [c167]Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han:
Negative Label Guided OOD Detection with Pretrained Vision-Language Models. ICLR 2024 - [c166]Cong Lei, Yuxuan Du, Peng Mi, Jun Yu, Tongliang Liu:
Neural Auto-designer for Enhanced Quantum Kernels. ICLR 2024 - [c165]Longkang Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang:
Federated Causal Discovery from Heterogeneous Data. ICLR 2024 - [c164]Xiu-Chuan Li, Kun Zhang, Tongliang Liu:
Causal Structure Recovery with Latent Variables under Milder Distributional and Graphical Assumptions. ICLR 2024 - [c163]Runqi Lin, Chaojian Yu, Bo Han, Tongliang Liu:
On the Over-Memorization During Natural, Robust and Catastrophic Overfitting. ICLR 2024 - [c162]Jun Nie, Yonggang Zhang, Zhen Fang, Tongliang Liu, Bo Han, Xinmei Tian:
Out-of-Distribution Detection with Negative Prompts. ICLR 2024 - [c161]Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xinmei Tian, Tongliang Liu, Bo Han, Xiaowen Chu:
FedImpro: Measuring and Improving Client Update in Federated Learning. ICLR 2024 - [c160]Suqin Yuan, Lei Feng, Tongliang Liu:
Early Stopping Against Label Noise Without Validation Data. ICLR 2024 - [c159]Shaokun Zhang, Xiaobo Xia, Zhaoqing Wang, Ling-Hao Chen, Jiale Liu, Qingyun Wu, Tongliang Liu:
IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models. ICLR 2024 - [c158]Yonggang Zhang, Zhiqin Yang, Xinmei Tian, Nannan Wang, Tongliang Liu, Bo Han:
Robust Training of Federated Models with Extremely Label Deficiency. ICLR 2024 - [c157]Jiyang Zheng, Yu Yao, Bo Han, Dadong Wang, Tongliang Liu:
Enhancing Contrastive Learning for Ordinal Regression via Ordinal Content Preserved Data Augmentation. ICLR 2024 - [c156]Pengfei Zheng, Yonggang Zhang, Zhen Fang, Tongliang Liu, Defu Lian, Bo Han:
NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation. ICLR 2024 - [c155]Chentao Cao, Zhun Zhong, Zhanke Zhou, Yang Liu, Tongliang Liu, Bo Han:
Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection. ICML 2024 - [c154]Yusong Hu, De Cheng, Dingwen Zhang, Nannan Wang, Tongliang Liu, Xinbo Gao:
Task-aware Orthogonal Sparse Network for Exploring Shared Knowledge in Continual Learning. ICML 2024 - [c153]Zhuo Huang, Chang Liu, Yinpeng Dong, Hang Su, Shibao Zheng, Tongliang Liu:
Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning. ICML 2024 - [c152]Muyang Li, Xiaobo Xia, Runze Wu, Fengming Huang, Jun Yu, Bo Han, Tongliang Liu:
Towards Realistic Model Selection for Semi-supervised Learning. ICML 2024 - [c151]Runqi Lin, Chaojian Yu, Bo Han, Hang Su, Tongliang Liu:
Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency. ICML 2024 - [c150]Hongduan Tian, Feng Liu, Tongliang Liu, Bo Du, Yiu-ming Cheung, Bo Han:
MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence. ICML 2024 - [c149]Wenjie Wang, Biwei Huang, Feng Liu, Xinge You, Tongliang Liu, Kun Zhang, Mingming Gong:
Optimal Kernel Choice for Score Function-based Causal Discovery. ICML 2024 - [c148]Yuhao Wu, Jiangchao Yao, Bo Han, Lina Yao, Tongliang Liu:
Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning. ICML 2024 - [c147]Yuhao Wu, Jiangchao Yao, Xiaobo Xia, Jun Yu, Ruxin Wang, Bo Han, Tongliang Liu:
Mitigating Label Noise on Graphs via Topological Sample Selection. ICML 2024 - [c146]Xiaobo Xia, Jiale Liu, Shaokun Zhang, Qingyun Wu, Hongxin Wei, Tongliang Liu:
Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints. ICML 2024 - [c145]Jiacheng Zhang, Feng Liu, Dawei Zhou, Jingfeng Zhang, Tongliang Liu:
Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training. ICML 2024 - [e2]Tongliang Liu, Geoffrey I. Webb, Lin Yue, Dadong Wang:
AI 2023: Advances in Artificial Intelligence - 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, November 28 - December 1, 2023, Proceedings, Part I. Lecture Notes in Computer Science 14471, Springer 2024, ISBN 978-981-99-8387-2 [contents] - [e1]Tongliang Liu, Geoffrey I. Webb, Lin Yue, Dadong Wang:
AI 2023: Advances in Artificial Intelligence - 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, November 28 - December 1, 2023, Proceedings, Part II. Lecture Notes in Computer Science 14472, Springer 2024, ISBN 978-981-99-8390-2 [contents] - [i209]Xue Dong, Xuemeng Song, Tongliang Liu, Weili Guan:
Prompt-based Multi-interest Learning Method for Sequential Recommendation. CoRR abs/2401.04312 (2024) - [i208]Qiang Qu, Yiran Shen, Xiaoming Chen, Yuk Ying Chung, Tongliang Liu:
E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning. CoRR abs/2401.08117 (2024) - [i207]Guanglin Zhou, Zhongyi Han, Shiming Chen, Biwei Huang, Liming Zhu, Tongliang Liu, Lina Yao, Kun Zhang:
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization. CoRR abs/2401.09716 (2024) - [i206]Cong Lei, Yuxuan Du, Peng Mi, Jun Yu, Tongliang Liu:
Neural auto-designer for enhanced quantum kernels. CoRR abs/2401.11098 (2024) - [i205]Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang:
Discovery of the Hidden World with Large Language Models. CoRR abs/2402.03941 (2024) - [i204]Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xinmei Tian, Tongliang Liu, Bo Han, Xiaowen Chu:
FedImpro: Measuring and Improving Client Update in Federated Learning. CoRR abs/2402.07011 (2024) - [i203]Zhaoqing Wang, Xiaobo Xia, Ziye Chen, Xiao He, Yandong Guo, Mingming Gong, Tongliang Liu:
Open-Vocabulary Segmentation with Unpaired Mask-Text Supervision. CoRR abs/2402.08960 (2024) - [i202]Loka Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang:
Federated Causal Discovery from Heterogeneous Data. CoRR abs/2402.13241 (2024) - [i201]Yonggang Zhang, Zhiqin Yang, Xinmei Tian, Nannan Wang, Tongliang Liu, Bo Han:
Robust Training of Federated Models with Extremely Label Deficiency. CoRR abs/2402.14430 (2024) - [i200]Rong Dai, Yonggang Zhang, Ang Li, Tongliang Liu, Xun Yang, Bo Han:
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting. CoRR abs/2402.15070 (2024) - [i199]Yuhao Wu, Jiangchao Yao, Xiaobo Xia, Jun Yu, Ruxin Wang, Bo Han, Tongliang Liu:
Mitigating Label Noise on Graph via Topological Sample Selection. CoRR abs/2403.01942 (2024) - [i198]Pengfei Zheng, Yonggang Zhang, Zhen Fang, Tongliang Liu, Defu Lian, Bo Han:
NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation. CoRR abs/2403.08840 (2024) - [i197]Jingyi Wang, Xiaobo Xia, Long Lan, Xinghao Wu, Jun Yu, Wenjing Yang, Bo Han, Tongliang Liu:
Tackling Noisy Labels with Network Parameter Additive Decomposition. CoRR abs/2403.13241 (2024) - [i196]Yiwei Zhou, Xiaobo Xia, Zhiwei Lin, Bo Han, Tongliang Liu:
Few-Shot Adversarial Prompt Learning on Vision-Language Models. CoRR abs/2403.14774 (2024) - [i195]Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han:
Negative Label Guided OOD Detection with Pretrained Vision-Language Models. CoRR abs/2403.20078 (2024) - [i194]Zhuo Li, He Zhao, Zhen Li, Tongliang Liu, Dandan Guo, Xiang Wan:
Extracting Clean and Balanced Subset for Noisy Long-tailed Classification. CoRR abs/2404.06795 (2024) - [i193]Runqi Lin, Chaojian Yu, Tongliang Liu:
Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization. CoRR abs/2404.08154 (2024) - [i192]Yuxiang Zheng, Zhongyi Han, Yilong Yin, Xin Gao, Tongliang Liu:
Can We Treat Noisy Labels as Accurate? CoRR abs/2405.12969 (2024) - [i191]Run Luo, Yunshui Li, Longze Chen, Wanwei He, Ting-En Lin, Ziqiang Liu, Lei Zhang, Zikai Song, Xiaobo Xia, Tongliang Liu, Min Yang, Binyuan Hui:
DEEM: Diffusion Models Serve as the Eyes of Large Language Models for Image Perception. CoRR abs/2405.15232 (2024) - [i190]Runqi Lin, Chaojian Yu, Bo Han, Hang Su, Tongliang Liu:
Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency. CoRR abs/2405.16262 (2024) - [i189]Hongwei Bran Li, Fernando Navarro, Ivan Ezhov, Amirhossein Bayat, Dhritiman Das, Florian Kofler, Suprosanna Shit, Diana Waldmannstetter, Johannes C. Paetzold, Xiaobin Hu, Benedikt Wiestler, Lucas Zimmer, Tamaz Amiranashvili, Chinmay Prabhakar, Christoph Berger, Jonas Weidner, Michelle Alonso-Basanta, Arif Rashid, Ujjwal Baid, Wesam Adel, Deniz Alis, Bhakti Baheti, Yingbin Bai, Ishaan Bhat, Sabri Can Cetindag, Wenting Chen, Li Cheng, Prasad Dutande, Lara Dular, Mustafa A. Elattar, Ming Feng, Shengbo Gao, Henkjan Huisman, Weifeng Hu, Shubham Innani, Wei Jiat, Davood Karimi, Hugo J. Kuijf, Jin Tae Kwak, Hoang Long Le, Xiang Lia, Huiyan Lin, Tongliang Liu, Jun Ma, Kai Ma, Ting Ma, Ilkay Öksüz, Robbie Holland, Arlindo L. Oliveira, Jimut Bahan Pal, Xuan Pei, Maoying Qiao, Anindo Saha, Raghavendra Selvan, Linlin Shen, João Lourenço Silva, Ziga Spiclin, Sanjay N. Talbar, Dadong Wang, Wei Wang, Xiong Wang, Yin Wang, Ruiling Xia, Kele Xu, Yanwu Yan, Mert Yergin, Shuang Yu, Lingxi Zeng, YingLin Zhang, Jiachen Zhao, Yefeng Zheng, Martin Zukovec, Richard K. G. Do, Anton S. Becker, Amber L. Simpson, Ender Konukoglu, András Jakab, Spyridon Bakas, Leo Joskowicz, Bjoern H. Menze:
QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge. CoRR abs/2405.18435 (2024) - [i188]Hongduan Tian, Feng Liu, Tongliang Liu, Bo Du, Yiu-ming Cheung, Bo Han:
MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence. CoRR abs/2405.18786 (2024) - [i187]Yuhao Wu, Jiangchao Yao, Bo Han, Lina Yao, Tongliang Liu:
Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning. CoRR abs/2405.19919 (2024) - [i186]Jiacheng Zhang, Feng Liu, Dawei Zhou, Jingfeng Zhang, Tongliang Liu:
Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training. CoRR abs/2406.00685 (2024) - [i185]Chentao Cao, Zhun Zhong, Zhanke Zhou, Yang Liu, Tongliang Liu, Bo Han:
Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection. CoRR abs/2406.00806 (2024) - [i184]Xiaoli Wei, Zhaoqing Wang, Yandong Guo, Chunxia Zhang, Tongliang Liu, Mingming Gong:
Training-Free Robust Interactive Video Object Segmentation. CoRR abs/2406.05485 (2024) - [i183]Qizhou Wang, Bo Han, Puning Yang, Jianing Zhu, Tongliang Liu, Masashi Sugiyama:
Unlearning with Control: Assessing Real-world Utility for Large Language Model Unlearning. CoRR abs/2406.09179 (2024) - [i182]Tianfu Wang, Li Shen, Qilin Fan, Tong Xu, Tongliang Liu, Hui Xiong:
Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning. CoRR abs/2406.17334 (2024) - [i181]Wenjie Wang, Biwei Huang, Feng Liu, Xinge You, Tongliang Liu, Kun Zhang, Mingming Gong:
Optimal Kernel Choice for Score Function-based Causal Discovery. CoRR abs/2407.10132 (2024) - [i180]Huaxi Huang, Xin Yu, Qiyu Liao, Dadong Wang, Tongliang Liu:
Enhancing User-Centric Privacy Protection: An Interactive Framework through Diffusion Models and Machine Unlearning. CoRR abs/2409.03326 (2024) - [i179]Yewen Li, Chaojie Wang, Xiaobo Xia, Xu He, Ruyi An, Dong Li, Tongliang Liu, Bo An, Xinrun Wang:
Resultant: Incremental Effectiveness on Likelihood for Unsupervised Out-of-Distribution Detection. CoRR abs/2409.03801 (2024) - [i178]Hongduan Tian, Feng Liu, Zhanke Zhou, Tongliang Liu, Chengqi Zhang, Bo Han:
Mind the Gap Between Prototypes and Images in Cross-domain Finetuning. CoRR abs/2410.12474 (2024) - [i177]Boxuan Zhang, Jianing Zhu, Zengmao Wang, Tongliang Liu, Bo Du, Bo Han:
What If the Input is Expanded in OOD Detection? CoRR abs/2410.18472 (2024) - [i176]Zhanke Zhou, Jianing Zhu, Fengfei Yu, Xuan Li, Xiong Peng, Tongliang Liu, Bo Han:
Model Inversion Attacks: A Survey of Approaches and Countermeasures. CoRR abs/2411.10023 (2024) - [i175]Zhaoqing Wang, Xiaobo Xia, Runnan Chen, Dongdong Yu, Changhu Wang, Mingming Gong, Tongliang Liu:
LaVin-DiT: Large Vision Diffusion Transformer. CoRR abs/2411.11505 (2024) - [i174]Zhenchen Wan, Yanwu Xu, Zhaoqing Wang, Feng Liu, Tongliang Liu, Mingming Gong:
TED-VITON: Transformer-Empowered Diffusion Models for Virtual Try-On. CoRR abs/2411.17017 (2024) - [i173]Yuxin Tian, Mouxing Yang, Yuhao Zhou, Jian Wang, Qing Ye, Tongliang Liu, Gang Niu, Jiancheng Lv:
Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels. CoRR abs/2412.00452 (2024) - [i172]Ziwen Li, Jiaxin Huang, Runnan Chen, Yunlong Che, Yandong Guo, Tongliang Liu, Fakhri Karray, Mingming Gong:
Urban4D: Semantic-Guided 4D Gaussian Splatting for Urban Scene Reconstruction. CoRR abs/2412.03473 (2024) - [i171]Jun Nie, Yonggang Zhang, Tongliang Liu, Yiu-ming Cheung, Bo Han, Xinmei Tian:
Detecting Discrepancies Between AI-Generated and Natural Images Using Uncertainty. CoRR abs/2412.05897 (2024) - [i170]Weijie Tu, Weijian Deng, Dylan Campbell, Yu Yao, Jiyang Zheng, Tom Gedeon, Tongliang Liu:
Ranked from Within: Ranking Large Multimodal Models for Visual Question Answering Without Labels. CoRR abs/2412.06461 (2024) - 2023
- [j75]Chuang Liu, Yibing Zhan, Baosheng Yu, Liu Liu, Bo Du, Wenbin Hu, Tongliang Liu:
On exploring node-feature and graph-structure diversities for node drop graph pooling. Neural Networks 167: 559-571 (2023) - [j74]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) - [j73]Xiaoqing Guo, Jie Liu, Tongliang Liu, Yixuan Yuan:
Handling Open-Set Noise and Novel Target Recognition in Domain Adaptive Semantic Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 45(8): 9846-9861 (2023) - [j72]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. IEEE Trans. Pattern Anal. Mach. Intell. 45(10): 12321-12340 (2023) - [j71]Shuo Yang, Songhua Wu, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu:
A Parametrical Model for Instance-Dependent Label Noise. IEEE Trans. Pattern Anal. Mach. Intell. 45(12): 14055-14068 (2023) - [j70]Xiu-Chuan Li, Xiaobo Xia, Fei Zhu, Tongliang Liu, Xu-Yao Zhang, Cheng-Lin Liu:
Dynamics-aware loss for learning with label noise. Pattern Recognit. 144: 109835 (2023) - [j69]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) - [j68]Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, Yixuan Li, Junzhou Huang:
Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions. Trans. Mach. Learn. Res. 2023 (2023) - [j67]Erdun Gao, Junjia Chen, Li Shen, Tongliang Liu, Mingming Gong, Howard D. Bondell:
FedDAG: Federated DAG Structure Learning. Trans. Mach. Learn. Res. 2023 (2023) - [j66]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) - [j65]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) - [j64]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) - [j63]Jingwei Zhang, Tongliang Liu, Dacheng Tao:
An Optimal Transport Analysis on Generalization in Deep Learning. IEEE Trans. Neural Networks Learn. Syst. 34(6): 2842-2853 (2023) - [j62]