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De-Chuan Zhan
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2020 – today
- 2025
- [j33]Yi Shi, Han-Jia Ye, Dongliang Man, Xiaoxu Han, De-Chuan Zhan, Yuan Jiang:
Revisiting multi-dimensional classification from a dimension-wise perspective. Frontiers Comput. Sci. 19(1): 191304 (2025) - 2024
- [j32]Da-Wei Zhou, Zhi-Hong Qi, Han-Jia Ye, De-Chuan Zhan:
TV100: a TV series dataset that pre-trained CLIP has not seen. Frontiers Comput. Sci. 18(5): 185349 (2024) - [j31]Lan Li, De-Chuan Zhan
, Xin-Chun Li:
Aligning model outputs for class imbalanced non-IID federated learning. Mach. Learn. 113(4): 1861-1884 (2024) - [j30]Han-Jia Ye
Contextualizing Meta-Learning via Learning to Decompose. IEEE Trans. Pattern Anal. Mach. Intell. 46(1): 117-133 (2024) - [j29]Han-Jia Ye
Few-Shot Learning With a Strong Teacher. IEEE Trans. Pattern Anal. Mach. Intell. 46(3): 1425-1440 (2024) - [j28]Yang Yang
Robust Semi-Supervised Learning by Wisely Leveraging Open-Set Data. IEEE Trans. Pattern Anal. Mach. Intell. 46(12): 8334-8347 (2024) - [j27]Da-Wei Zhou
Class-Incremental Learning: A Survey. IEEE Trans. Pattern Anal. Mach. Intell. 46(12): 9851-9873 (2024) - [j26]Xin-Chun Li
MAP: Model Aggregation and Personalization in Federated Learning With Incomplete Classes. IEEE Trans. Knowl. Data Eng. 36(11): 6560-6573 (2024) - [j25]Lu Han
The Capacity and Robustness Trade-Off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting. IEEE Trans. Knowl. Data Eng. 36(11): 7129-7142 (2024) - [c90]Lan Li, Bowen Tao, Lu Han, De-Chuan Zhan, Han-Jia Ye:
Twice Class Bias Correction for Imbalanced Semi-supervised Learning. AAAI 2024: 13563-13571 - [c89]Da-Wei Zhou, Hai-Long Sun, Han-Jia Ye, De-Chuan Zhan:
Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning. CVPR 2024: 23554-23564 - [c88]Chao Yi, Lu Ren, De-Chuan Zhan, Han-Jia Ye:
Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification. CVPR 2024: 27392-27401 - [c87]Yichu Xu, Xin-Chun Li, Le Gan, De-Chuan Zhan:
Weight Scope Alignment: A Frustratingly Easy Method for Model Merging. ECAI 2024: 1720-1727 - [c86]Bowen Tao, Lan Li, Xin-Chun Li, De-Chuan Zhan:
CLAF: Contrastive Learning with Augmented Features for Imbalanced Semi-Supervised Learning. ICASSP 2024: 7185-7189 - [c85]Wen-Shu Fan, Su Lu, Xin-Chun Li, De-Chuan Zhan, Le Gan:
Revisit the Essence of Distilling Knowledge through Calibration. ICML 2024 - [c84]Lu Han, Han-Jia Ye, De-Chuan Zhan:
SIN: Selective and Interpretable Normalization for Long-Term Time Series Forecasting. ICML 2024 - [c83]Jun-Peng Jiang, Han-Jia Ye, Leye Wang, Yang Yang, Yuan Jiang, De-Chuan Zhan:
Tabular Insights, Visual Impacts: Transferring Expertise from Tables to Images. ICML 2024 - [c82]Lan Li, Xin-Chun Li, Han-Jia Ye, De-Chuan Zhan:
Enhancing Class-Imbalanced Learning with Pre-Trained Guidance through Class-Conditional Knowledge Distillation. ICML 2024 - [c81]Bowen Tao, Xin-Chun Li, De-Chuan Zhan:
MLI Formula: A Nearly Scale-Invariant Solution with Noise Perturbation. ICML 2024 - [c80]Shenghua Wan, Ziyuan Chen, Le Gan, Shuai Feng, De-Chuan Zhan:
SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets. ICML 2024 - [c79]Yucen Wang, Shenghua Wan, Le Gan, Shuai Feng, De-Chuan Zhan:
AD3: Implicit Action is the Key for World Models to Distinguish the Diverse Visual Distractors. ICML 2024 - [c78]Bowen Zheng, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Multi-layer Rehearsal Feature Augmentation for Class-Incremental Learning. ICML 2024 - [c77]Shenghua Wan, Hai-Hang Sun, Le Gan, De-Chuan Zhan:
MOSER: Learning Sensory Policy for Task-specific Viewpoint via View-conditional World Model. IJCAI 2024: 5046-5054 - [c76]Da-Wei Zhou, Hai-Long Sun, Jingyi Ning, Han-Jia Ye, De-Chuan Zhan:
Continual Learning with Pre-Trained Models: A Survey. IJCAI 2024: 8363-8371 - [c75]Yi Shi, Xu-Peng Tian, Yun-Kai Wang, Tie-Yi Zhang, Bing Yao, Hui Wang, Yong Shao, Cen-Cen Wang, Rong Zeng, De-Chuan Zhan:
CS3: Cascade SAM for Sperm Segmentation. MICCAI (3) 2024: 596-605 - [i78]Da-Wei Zhou, Hai-Long Sun, Jingyi Ning, Han-Jia Ye, De-Chuan Zhan:
Continual Learning with Pre-Trained Models: A Survey. CoRR abs/2401.16386 (2024) - [i77]Yucen Wang, Shenghua Wan, Le Gan, Shuai Feng, De-Chuan Zhan:
AD3: Implicit Action is the Key for World Models to Distinguish the Diverse Visual Distractors. CoRR abs/2403.09976 (2024) - [i76]Da-Wei Zhou, Hai-Long Sun, Han-Jia Ye, De-Chuan Zhan:
Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning. CoRR abs/2403.12030 (2024) - [i75]Chao Yi, De-Chuan Zhan, Han-Jia Ye:
Bridge the Modality and Capacity Gaps in Vision-Language Model Selection. CoRR abs/2403.13797 (2024) - [i74]Kaichen Huang, Hai-Hang Sun, Shenghua Wan, Minghao Shao, Shuai Feng, Le Gan, De-Chuan Zhan:
DIDA: Denoised Imitation Learning based on Domain Adaptation. CoRR abs/2404.03382 (2024) - [i73]Kaichen Huang, Minghao Shao, Shenghua Wan, Hai-Hang Sun, Shuai Feng, Le Gan, De-Chuan Zhan:
SENSOR: Imitate Third-Person Expert's Behaviors via Active Sensoring. CoRR abs/2404.03386 (2024) - [i72]Xin-Chun Li, Shaoming Song, Yinchuan Li, Bingshuai Li, Yunfeng Shao, Yang Yang, De-Chuan Zhan:
MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes. CoRR abs/2404.09232 (2024) - [i71]Da-Wei Zhou, Zhi-Hong Qi, Han-Jia Ye, De-Chuan Zhan:
TV100: A TV Series Dataset that Pre-Trained CLIP Has Not Seen. CoRR abs/2404.12407 (2024) - [i70]Lu Han, Xu-Yang Chen, Han-Jia Ye, De-Chuan Zhan:
SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion. CoRR abs/2404.14197 (2024) - [i69]Chao Yi, Lu Ren, De-Chuan Zhan, Han-Jia Ye:
Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification. CoRR abs/2404.17753 (2024) - [i68]Yang Yang, Nan Jiang, Yi Xu, De-Chuan Zhan:
Robust Semi-supervised Learning by Wisely Leveraging Open-set Data. CoRR abs/2405.06979 (2024) - [i67]Xin-Chun Li, Jin-Lin Tang, Bo Zhang, Lan Li, De-Chuan Zhan:
Exploring and Exploiting the Asymmetric Valley of Deep Neural Networks. CoRR abs/2405.12489 (2024) - [i66]Xin-Chun Li, Lan Li, De-Chuan Zhan:
Visualizing, Rethinking, and Mining the Loss Landscape of Deep Neural Networks. CoRR abs/2405.12493 (2024) - [i65]Xin-Chun Li, Wen-Shu Fan, Bowen Tao, Le Gan, De-Chuan Zhan:
Exploring Dark Knowledge under Various Teacher Capacities and Addressing Capacity Mismatch. CoRR abs/2405.13078 (2024) - [i64]Hai-Long Sun, Da-Wei Zhou, Yang Li, Shiyin Lu, Chao Yi, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, De-Chuan Zhan, Han-Jia Ye:
Parrot: Multilingual Visual Instruction Tuning. CoRR abs/2406.02539 (2024) - [i63]Yi-Kai Zhang, Shiyin Lu, Yang Li, Yanqing Ma, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, De-Chuan Zhan, Han-Jia Ye:
Wings: Learning Multimodal LLMs without Text-only Forgetting. CoRR abs/2406.03496 (2024) - [i62]Ting-Ji Huang, Jia-Qi Yang, Chunxu Shen, Kai-Qi Liu, De-Chuan Zhan, Han-Jia Ye:
Improving LLMs for Recommendation with Out-Of-Vocabulary Tokens. CoRR abs/2406.08477 (2024) - [i61]Shenghua Wan, Ziyuan Chen, Le Gan, Shuai Feng, De-Chuan Zhan:
SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets. CoRR abs/2406.09486 (2024) - [i60]Han-Jia Ye, Si-Yang Liu, Hao-Run Cai, Qi-Le Zhou, De-Chuan Zhan:
A Closer Look at Deep Learning on Tabular Data. CoRR abs/2407.00956 (2024) - [i59]Han-Jia Ye, Huai-Hong Yin, De-Chuan Zhan:
Modern Neighborhood Components Analysis: A Deep Tabular Baseline Two Decades Later. CoRR abs/2407.03257 (2024) - [i58]Yi Shi, Xu-Peng Tian, Yun-Kai Wang, Tie-Yi Zhang, Bin Yao, Hui Wang, Yong Shao, Cen-Cen Wang, Rong Zeng, De-Chuan Zhan:
CS3: Cascade SAM for Sperm Segmentation. CoRR abs/2407.03772 (2024) - [i57]Yichu Xu, Xin-Chun Li, Le Gan, De-Chuan Zhan:
Weight Scope Alignment: A Frustratingly Easy Method for Model Merging. CoRR abs/2408.12237 (2024) - [i56]Zhi-Hong Qi, Da-Wei Zhou, Yiran Yao, Han-Jia Ye, De-Chuan Zhan:
Adaptive Adapter Routing for Long-Tailed Class-Incremental Learning. CoRR abs/2409.07446 (2024) - [i55]Da-Wei Zhou, Zi-Wen Cai, Han-Jia Ye, Lijun Zhang, De-Chuan Zhan:
Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning. CoRR abs/2410.00911 (2024) - 2023
- [j24]Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, De-Chuan Zhan:
PyCIL: a Python toolbox for class-incremental learning. Sci. China Inf. Sci. 66(9) (2023) - [j23]Yang Yang, Ran Bao, Weili Guo, De-Chuan Zhan, Yilong Yin, Jian Yang:
Deep visual-linguistic fusion network considering cross-modal inconsistency for rumor detection. Sci. China Inf. Sci. 66(12) (2023) - [j22]Xin-Chun Li
MrTF: model refinery for transductive federated learning. Data Min. Knowl. Discov. 37(5): 2046-2069 (2023) - [j21]Han-Jia Ye
Generalized Knowledge Distillation via Relationship Matching. IEEE Trans. Pattern Anal. Mach. Intell. 45(2): 1817-1834 (2023) - [j20]Han-Jia Ye
Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks. IEEE Trans. Pattern Anal. Mach. Intell. 45(3): 3721-3737 (2023) - [j19]Da-Wei Zhou
Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks. IEEE Trans. Pattern Anal. Mach. Intell. 45(11): 12816-12831 (2023) - [j18]Yang Yang
Corporate Relative Valuation Using Heterogeneous Multi-Modal Graph Neural Network. IEEE Trans. Knowl. Data Eng. 35(1): 211-224 (2023) - [j17]Yang Yang
Cost-Effective Incremental Deep Model: Matching Model Capacity With the Least Sampling. IEEE Trans. Knowl. Data Eng. 35(4): 3575-3588 (2023) - [c74]Ting-Ji Huang, Qi-Le Zhou, Han-Jia Ye, De-Chuan Zhan:
Change Point Detection via Synthetic Signals. AALTD@ECML/PKDD 2023: 25-35 - [c73]Shaowei Zhang, Jiahan Cao, Lei Yuan, Yang Yu, De-Chuan Zhan:
Self-Motivated Multi-Agent Exploration. AAMAS 2023: 476-484 - [c72]Yi-Kai Zhang, Qi-Wei Wang, De-Chuan Zhan, Han-Jia Ye:
Learning Debiased Representations via Conditional Attribute Interpolation. CVPR 2023: 7599-7608 - [c71]Da-Wei Zhou, Qi-Wei Wang, Han-Jia Ye, De-Chuan Zhan:
A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning. ICLR 2023 - [c70]Lu Han, Han-Jia Ye, De-Chuan Zhan:
Augmentation Component Analysis: Modeling Similarity via the Augmentation Overlaps. ICLR 2023 - [c69]Fu-Yun Wang, Da-Wei Zhou, Liu Liu, Han-Jia Ye, Yatao Bian, De-Chuan Zhan, Peilin Zhao:
BEEF: Bi-Compatible Class-Incremental Learning via Energy-Based Expansion and Fusion. ICLR 2023 - [c68]Yichu Xu, Wenqian Li, Yinchuan Li, Yunfeng Shao, Yan Pang, De-Chuan Zhan:
One Important Thing To Do Before Federated Training. Tiny Papers @ ICLR 2023 - [c67]Chao Yi, Ting-Ji Huang, Han-Jia Ye, De-Chuan Zhan:
Improved Dynamic Spatial-Temporal Attention Network for Early Anticipation of Traffic Accidents. ICME Workshops 2023: 81-86 - [c66]Bowen Zheng, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Preserving Locality in Vision Transformers for Class Incremental Learning. ICME 2023: 1157-1162 - [c65]Shenghua Wan, Yucen Wang, Minghao Shao, Ruying Chen, De-Chuan Zhan:
SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models. ICML 2023: 35426-35443 - [c64]Jia-Qi Yang
IDToolkit: A Toolkit for Benchmarking and Developing Inverse Design Algorithms in Nanophotonics. KDD 2023: 2930-2940 - [c63]Yi Shi, Rui-Xiang Li, Wen-Qi Shao, Xin-Cen Duan, Han-Jia Ye, De-Chuan Zhan, Bai-Shen Pan, Bei-Li Wang, Wei Guo, Yuan Jiang:
A Multi-task Method for Immunofixation Electrophoresis Image Classification. MICCAI (6) 2023: 148-158 - [c62]Qi-Wei Wang, Da-Wei Zhou, Yi-Kai Zhang, De-Chuan Zhan, Han-Jia Ye:
Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration. NeurIPS 2023 - [c61]Jia-Qi Yang, De-Chuan Zhan, Le Gan:
Beyond probability partitions: Calibrating neural networks with semantic aware grouping. NeurIPS 2023 - [c60]Yi-Kai Zhang, Ting-Ji Huang, Yao-Xiang Ding, De-Chuan Zhan, Han-Jia Ye:
Model Spider: Learning to Rank Pre-Trained Models Efficiently. NeurIPS 2023 - [c59]Jun-Peng Jiang, Han-Jia Ye, Leye Wang, Yang Yang, Yuan Jiang, De-Chuan Zhan:
On Transferring Expert Knowledge from Tabular Data to Images. UniReps 2023: 102-115 - [i54]Shaowei Zhang, Jiahan Cao, Lei Yuan, Yang Yu, De-Chuan Zhan:
Self-Motivated Multi-Agent Exploration. CoRR abs/2301.02083 (2023) - [i53]Lu Han, Han-Jia Ye, De-Chuan Zhan:
On Pseudo-Labeling for Class-Mismatch Semi-Supervised Learning. CoRR abs/2301.06010 (2023) - [i52]Da-Wei Zhou, Qi-Wei Wang, Zhi-Hong Qi, Han-Jia Ye, De-Chuan Zhan, Ziwei Liu:
Deep Class-Incremental Learning: A Survey. CoRR abs/2302.03648 (2023) - [i51]Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan, Ziwei Liu:
Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need. CoRR abs/2303.07338 (2023) - [i50]Lu Han, Han-Jia Ye, De-Chuan Zhan:
The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting. CoRR abs/2304.05206 (2023) - [i49]Bowen Zheng, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Preserving Locality in Vision Transformers for Class Incremental Learning. CoRR abs/2304.06971 (2023) - [i48]Xin-Chun Li, Yang Yang, De-Chuan Zhan:
MrTF: Model Refinery for Transductive Federated Learning. CoRR abs/2305.04201 (2023) - [i47]Jia-Qi Yang, Yucheng Xu, Jia-Lei Shen, Ke-Bin Fan, De-Chuan Zhan, Yang Yang:
IDToolkit: A Toolkit for Benchmarking and Developing Inverse Design Algorithms in Nanophotonics. CoRR abs/2305.18978 (2023) - [i46]Da-Wei Zhou, Yuanhan Zhang, Jingyi Ning, Han-Jia Ye, De-Chuan Zhan, Ziwei Liu:
Learning without Forgetting for Vision-Language Models. CoRR abs/2305.19270 (2023) - [i45]Yi-Kai Zhang, Ting-Ji Huang, Yao-Xiang Ding, De-Chuan Zhan, Han-Jia Ye:
Model Spider: Learning to Rank Pre-Trained Models Efficiently. CoRR abs/2306.03900 (2023) - [i44]Jia-Qi Yang, De-Chuan Zhan, Le Gan:
Beyond Probability Partitions: Calibrating Neural Networks with Semantic Aware Grouping. CoRR abs/2306.04985 (2023) - [i43]Jia-Qi Yang, Chenglei Dai, Dan Ou, Ju Huang, De-Chuan Zhan, Qingwen Liu, Xiaoyi Zeng, Yang Yang:
COURIER: Contrastive User Intention Reconstruction for Large-Scale Pre-Train of Image Features. CoRR abs/2306.05001 (2023) - [i42]Shenghua Wan, Yucen Wang, Minghao Shao, Ruying Chen, De-Chuan Zhan:
SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models. CoRR abs/2306.10695 (2023) - [i41]Qi-Wei Wang, Hongyu Lu, Yu Chen, Da-Wei Zhou, De-Chuan Zhan, Ming Chen, Han-Jia Ye:
Streaming CTR Prediction: Rethinking Recommendation Task for Real-World Streaming Data. CoRR abs/2307.07509 (2023) - [i40]Yi-Kai Zhang, Lu Ren, Chao Yi, Qi-Wei Wang, De-Chuan Zhan, Han-Jia Ye:
ZhiJian: A Unifying and Rapidly Deployable Toolbox for Pre-trained Model Reuse. CoRR abs/2308.09158 (2023) - [i39]Hai-Long Sun, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
PILOT: A Pre-Trained Model-Based Continual Learning Toolbox. CoRR abs/2309.07117 (2023) - [i38]Songli Wu, Liang Du, Jia-Qi Yang, Yuai Wang, De-Chuan Zhan, Shuang Zhao, Zixun Sun:
REFORM: Removing False Correlation in Multi-level Interaction for CTR Prediction. CoRR abs/2309.14891 (2023) - [i37]Qi-Le Zhou, Han-Jia Ye, Leye Wang, De-Chuan Zhan:
Unlocking the Transferability of Tokens in Deep Models for Tabular Data. CoRR abs/2310.15149 (2023) - [i36]Han-Jia Ye, Qi-Le Zhou, De-Chuan Zhan:
Training-Free Generalization on Heterogeneous Tabular Data via Meta-Representation. CoRR abs/2311.00055 (2023) - [i35]Lu Han, Xu-Yang Chen, Han-Jia Ye, De-Chuan Zhan:
Learning Robust Precipitation Forecaster by Temporal Frame Interpolation. CoRR abs/2311.18341 (2023) - [i34]Qi-Wei Wang, Da-Wei Zhou, Yi-Kai Zhang, De-Chuan Zhan, Han-Jia Ye:
Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration. CoRR abs/2312.05229 (2023) - [i33]Bowen Tao, Lan Li, Xin-Chun Li, De-Chuan Zhan:
CLAF: Contrastive Learning with Augmented Features for Imbalanced Semi-Supervised Learning. CoRR abs/2312.09598 (2023) - [i32]Lan Li, Bowen Tao, Lu Han, De-Chuan Zhan, Han-Jia Ye:
Twice Class Bias Correction for Imbalanced Semi-Supervised Learning. CoRR abs/2312.16604 (2023) - 2022
- [j16]Lu Han, Han-Jia Ye, De-Chuan Zhan:
On Pseudo-Labeling for Class-Mismatch Semi-Supervised Learning. Trans. Mach. Learn. Res. 2022 (2022) - [j15]Da-Wei Zhou
Learning to Classify With Incremental New Class. IEEE Trans. Neural Networks Learn. Syst. 33(6): 2429-2443 (2022) - [c58]Jia-Qi Yang
RID-Noise: Towards Robust Inverse Design under Noisy Environments. AAAI 2022: 4654-4661 - [c57]Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, Liang Ma
Forward Compatible Few-Shot Class-Incremental Learning. CVPR 2022: 9036-9046 - [c56]Xin-Chun Li, Yichu Xu, Shaoming Song, Bingshuai Li, Yinchuan Li
Federated Learning with Position-Aware Neurons. CVPR 2022: 10072-10081 - [c55]Han-Jia Ye, Yi Shi, De-Chuan Zhan:
Identifying Ambiguous Similarity Conditions via Semantic Matching. CVPR 2022: 16589-16598 - [c54]Fu-Yun Wang
FOSTER: Feature Boosting and Compression for Class-Incremental Learning. ECCV (25) 2022: 398-414 - [c53]Xin-Chun Li, Yan-Jia Wang, Le Gan, De-Chuan Zhan:
Exploring Transferability Measures and Domain Selection in Cross-Domain Slot Filling. ICASSP 2022: 3758-3762 - [c52]Yi-Kai Zhang, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Audio-Visual Generalized Few-Shot Learning with Prototype-Based Co-Adaptation. INTERSPEECH 2022: 531-535 - [c51]Xin-Chun Li, Jin-Lin Tang, Shaoming Song, Bingshuai Li, Yinchuan Li
Avoid Overfitting User Specific Information in Federated Keyword Spotting. INTERSPEECH 2022: 3869-3873 - [c50]Xin Han
Streaming Hierarchical Clustering Based on Point-Set Kernel. KDD 2022: 525-533 - [c49]Jia-Qi Yang, De-Chuan Zhan:
Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems. NeurIPS 2022 - [c48]Xin-Chun Li, Wen-Shu Fan, Shaoming Song, Yinchuan Li, Bingshuai Li, Yunfeng Shao, De-Chuan Zhan:
Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again. NeurIPS 2022 - [i31]Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, Liang Ma, Shiliang Pu, De-Chuan Zhan:
Forward Compatible Few-Shot Class-Incremental Learning. CoRR abs/2203.06953 (2022) - [i30]Xin-Chun Li, Yi-Chu Xu, Shaoming Song, Bingshuai Li, Yinchuan Li, Yunfeng Shao, De-Chuan Zhan:
Federated Learning with Position-Aware Neurons. CoRR abs/2203.14666 (2022) - [i29]Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks. CoRR abs/2203.17030 (2022) - [i28]Han-Jia Ye, Yi Shi, De-Chuan Zhan:
Identifying Ambiguous Similarity Conditions via Semantic Matching. CoRR abs/2204.04053 (2022) - [i27]Fu-Yun Wang, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
FOSTER: Feature Boosting and Compression for Class-Incremental Learning. CoRR abs/2204.04662 (2022) - [i26]Su Lu, Han-Jia Ye, De-Chuan Zhan:
Faculty Distillation with Optimal Transport. CoRR abs/2204.11526 (2022) - [i25]Han-Jia Ye, Su Lu, De-Chuan Zhan:
Generalized Knowledge Distillation via Relationship Matching. CoRR abs/2205.01915 (2022) - [i24]Da-Wei Zhou, Qi-Wei Wang, Han-Jia Ye, De-Chuan Zhan:
A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning. CoRR abs/2205.13218 (2022) - [i23]Jia-Qi Yang
Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems. CoRR abs/2206.00407 (2022) - [i22]Lu Han, Han-Jia Ye, De-Chuan Zhan:
Contrastive Principal Component Learning: Modeling Similarity by Augmentation Overlap. CoRR abs/2206.00471 (2022) - [i21]Xin-Chun Li, Jin-Lin Tang, Shaoming Song, Bingshuai Li, Yinchuan Li, Yunfeng Shao, Le Gan, De-Chuan Zhan:
Avoid Overfitting User Specific Information in Federated Keyword Spotting. CoRR abs/2206.08864 (2022) - [i20]Xin-Chun Li, Wen-Shu Fan, Shaoming Song, Yinchuan Li, Bingshuai Li, Yunfeng Shao, De-Chuan Zhan:
Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again. CoRR abs/2210.04427 (2022) - 2021
- [j14]Xin-Chun Li, De-Chuan Zhan, Jia-Qi Yang
Deep multiple instance selection. Sci. China Inf. Sci. 64(3) (2021) - [j13]Han-Jia Ye
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning. Int. J. Comput. Vis. 129(6): 1930-1953 (2021) - [j12]Han-Jia Ye
Heterogeneous Few-Shot Model Rectification With Semantic Mapping. IEEE Trans. Pattern Anal. Mach. Intell. 43(11): 3878-3891 (2021) - [j11]Yang Yang
Semi-Supervised Multi-Modal Clustering and Classification with Incomplete Modalities. IEEE Trans. Knowl. Data Eng. 33(2): 682-695 (2021) - [j10]Yang Yang
Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport. IEEE Trans. Knowl. Data Eng. 33(2): 696-709 (2021) - [c47]Jia-Qi Yang
Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling. AAAI 2021: 4582-4589 - [c46]Su Lu, Han-Jia Ye, De-Chuan Zhan:
Tailoring Embedding Function to Heterogeneous Few-Shot Tasks by Global and Local Feature Adaptors. AAAI 2021: 8776-8783 - [c45]Han-Jia Ye, Xin-Chun Li, De-Chuan Zhan:
Task Cooperation for Semi-Supervised Few-Shot Learning. AAAI 2021: 10682-10690 - [c44]Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Learning Placeholders for Open-Set Recognition. CVPR 2021: 4401-4410 - [c43]Han-Jia Ye, De-Chuan Zhan, Wei-Lun Chao:
Procrustean Training for Imbalanced Deep Learning. ICCV 2021: 92-102 - [c42]Yang Yang, Chubing Zhang, Yi-Chu Xu, Dianhai Yu, De-Chuan Zhan, Jian Yang:
Rethinking Label-Wise Cross-Modal Retrieval from A Semantic Sharing Perspective. IJCAI 2021: 3300-3306 - [c41]Cheng Hang, Wei Wang, De-Chuan Zhan:
Multi-Modal Multi-Instance Multi-Label Learning with Graph Convolutional Network. IJCNN 2021: 1-8 - [c40]Xin-Chun Li, De-Chuan Zhan:
FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data. KDD 2021: 995-1005 - [c39]Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Co-Transport for Class-Incremental Learning. ACM Multimedia 2021: 1645-1654 - [c38]Su Lu, Han-Jia Ye, Le Gan, De-Chuan Zhan:
Towards Enabling Meta-Learning from Target Models. NeurIPS 2021: 8060-8071 - [c37]Da-Wei Zhou, Yang Yang, De-Chuan Zhan:
Detecting Sequentially Novel Classes with Stable Generalization Ability. PAKDD (1) 2021: 371-382 - [c36]Xin-Chun Li
FedPHP: Federated Personalization with Inherited Private Models. ECML/PKDD (1) 2021: 587-602 - [i19]Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Learning Placeholders for Open-Set Recognition. CoRR abs/2103.15086 (2021) - [i18]Han-Jia Ye, De-Chuan Zhan, Wei-Lun Chao:
Procrustean Training for Imbalanced Deep Learning. CoRR abs/2104.01769 (2021) - [i17]Su Lu, Han-Jia Ye, De-Chuan Zhan:
Support-Target Protocol for Meta-Learning. CoRR abs/2104.03736 (2021) - [i16]Su Lu, Han-Jia Ye, De-Chuan Zhan:
Few-Shot Action Recognition with Compromised Metric via Optimal Transport. CoRR abs/2104.03737 (2021) - [i15]Yang Yang, Zhao-Yang Fu, De-Chuan Zhan, Zhi-Bin Liu, Yuan Jiang:
Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport. CoRR abs/2104.08489 (2021) - [i14]Han-Jia Ye, Da-Wei Zhou, Lanqing Hong, Zhenguo Li, Xiu-Shen Wei, De-Chuan Zhan:
Contextualizing Multiple Tasks via Learning to Decompose. CoRR abs/2106.08112 (2021) - [i13]Han-Jia Ye, Lu Ming, De-Chuan Zhan, Wei-Lun Chao:
Few-Shot Learning with a Strong Teacher. CoRR abs/2107.00197 (2021) - [i12]Xin-Chun Li, Le Gan, De-Chuan Zhan, Yunfeng Shao, Bingshuai Li, Shaoming Song:
Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes. CoRR abs/2107.11954 (2021) - [i11]Xin-Chun Li, De-Chuan Zhan, Yunfeng Shao, Bingshuai Li, Shaoming Song:
Preliminary Steps Towards Federated Sentiment Classification. CoRR abs/2107.11956 (2021) - [i10]Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Co-Transport for Class-Incremental Learning. CoRR abs/2107.12654 (2021) - [i9]Jiahan Cao, Lei Yuan, Jianhao Wang, Shaowei Zhang, Chongjie Zhang, Yang Yu, De-Chuan Zhan:
LINDA: Multi-Agent Local Information Decomposition for Awareness of Teammates. CoRR abs/2109.12508 (2021) - [i8]Jia-Qi Yang, Ke-Bin Fan, Hao Ma, De-Chuan Zhan:
RID-Noise: Towards Robust Inverse Design under Noisy Environments. CoRR abs/2112.03912 (2021) - [i7]Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, De-Chuan Zhan:
PyCIL: A Python Toolbox for Class-Incremental Learning. CoRR abs/2112.12533 (2021) - 2020
- [j9]Yang Yang, Nengjun Zhu, Yi-Feng Wu, Jian Cao, De-Chuan Zhan, Hui Xiong:
A semi-supervised attention model for identifying authentic sneakers. Big Data Min. Anal. 3(1): 29-40 (2020) - [j8]Han-Jia Ye
Few-shot learning with adaptively initialized task optimizer: a practical meta-learning approach. Mach. Learn. 109(3): 643-664 (2020) - [j7]Han-Jia Ye
Learning Multiple Local Metrics: Global Consideration Helps. IEEE Trans. Pattern Anal. Mach. Intell. 42(7): 1698-1712 (2020) - [c35]Han-Jia Ye, Hexiang Hu
Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions. CVPR 2020: 8805-8814 - [c34]Han-Jia Ye, Su Lu, De-Chuan Zhan:
Distilling Cross-Task Knowledge via Relationship Matching. CVPR 2020: 12393-12402 - [c33]Jia-Qi Yang
Bottom-Up and Top-Down Graph Pooling. PAKDD (2) 2020: 568-579 - [c32]Xin-Chun Li, De-Chuan Zhan, Jia-Qi Yang
Towards Understanding Transfer Learning Algorithms Using Meta Transfer Features. PAKDD (2) 2020: 855-866 - [i6]Han-Jia Ye, Hong-You Chen, De-Chuan Zhan, Wei-Lun Chao:
Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning. CoRR abs/2001.01385 (2020) - [i5]Wei-Lun Chao, Han-Jia Ye, De-Chuan Zhan, Mark E. Campbell, Kilian Q. Weinberger:
Revisiting Meta-Learning as Supervised Learning. CoRR abs/2002.00573 (2020) - [i4]Han-Jia Ye, Lu Han, De-Chuan Zhan:
Revisiting Unsupervised Meta-Learning: Amplifying or Compensating for the Characteristics of Few-Shot Tasks. CoRR abs/2011.14663 (2020) - [i3]Jia-Qi Yang, Xiang Li, Shuguang Han, Tao Zhuang, De-Chuan Zhan, Xiaoyi Zeng, Bin Tong:
Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling. CoRR abs/2012.03245 (2020)
2010 – 2019
- 2019
- [j6]Han-Jia Ye, De-Chuan Zhan
Fast generalization rates for distance metric learning. Mach. Learn. 108(2): 267-295 (2019) - [j5]Han-Jia Ye
What Makes Objects Similar: A Unified Multi-Metric Learning Approach. IEEE Trans. Pattern Anal. Mach. Intell. 41(5): 1257-1270 (2019) - [c31]Xiang-Rong Sheng, De-Chuan Zhan, Su Lu, Yuan Jiang:
Multi-View Anomaly Detection: Neighborhood in Locality Matters. AAAI 2019: 4894-4901 - [c30]Yang Yang, Yi-Feng Wu, De-Chuan Zhan, Zhi-Bin Liu, Yuan Jiang:
Deep Robust Unsupervised Multi-Modal Network. AAAI 2019: 5652-5659 - [c29]Zhao-Yang Fu, De-Chuan Zhan, Xin-Chun Li, Yi-Xing Lu:
Automatic Successive Reinforcement Learning with Multiple Auxiliary Rewards. IJCAI 2019: 2336-2342 - [c28]Yang Yang, Ke-Tao Wang, De-Chuan Zhan, Hui Xiong, Yuan Jiang:
Comprehensive Semi-Supervised Multi-Modal Learning. IJCAI 2019: 4092-4098 - [c27]Yang Yang, Da-Wei Zhou
Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability. KDD 2019: 74-82 - [i2]Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, Fei Sha:
Learning Classifier Synthesis for Generalized Few-Shot Learning. CoRR abs/1906.02944 (2019) - 2018
- [c26]Yi-Feng Wu, De-Chuan Zhan, Yuan Jiang:
DMTMV: A Unified Learning Framework for Deep Multi-task Multi-view Learning. ICBK 2018: 49-56 - [c25]Xuan Huo, Yang Yang, Ming Li, De-Chuan Zhan:
Learning Semantic Features for Software Defect Prediction by Code Comments Embedding. ICDM 2018: 1049-1054 - [c24]Han-Jia Ye, De-Chuan Zhan, Yuan Jiang, Zhi-Hua Zhou:
Rectify Heterogeneous Models with Semantic Mapping. ICML 2018: 1904-1913 - [c23]Yang Yang, De-Chuan Zhan, Xiang-Rong Sheng, Yuan Jiang:
Semi-Supervised Multi-Modal Learning with Incomplete Modalities. IJCAI 2018: 2998-3004 - [c22]Han-Jia Ye, Xiang-Rong Sheng, De-Chuan Zhan, Peng He:
Distance Metric Facilitated Transportation between Heterogeneous Domains. IJCAI 2018: 3012-3018 - [c21]Yang Yang, Yi-Feng Wu, De-Chuan Zhan, Zhi-Bin Liu, Yuan Jiang:
Complex Object Classification: A Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport. KDD 2018: 2594-2603 - [c20]Yang Yang, De-Chuan Zhan, Yi-Feng Wu, Yuan Jiang:
Multi-network User Identification via Graph-Aware Embedding. PAKDD (2) 2018: 209-221 - [c19]Yang Yang, Yi-Feng Wu, De-Chuan Zhan, Yuan Jiang:
Deep Multi-modal Learning with Cascade Consensus. PRICAI 2018: 64-72 - [i1]Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, Fei Sha:
Learning Embedding Adaptation for Few-Shot Learning. CoRR abs/1812.03664 (2018) - 2017
- [c18]Yang Yang, De-Chuan Zhan, Ying Fan, Yuan Jiang, Zhi-Hua Zhou:
Deep Learning for Fixed Model Reuse. AAAI 2017: 2831-2837 - [c17]Yang Yang, De-Chuan Zhan, Ying Fan, Yuan Jiang:
Instance Specific Discriminative Modal Pursuit: A Serialized Approach. ACML 2017: 65-80 - [c16]Yang Yang, De-Chuan Zhan, Xiang-Yu Guo, Yuan Jiang:
Modal Consistency based Pre-Trained Multi-Model Reuse. IJCAI 2017: 3287-3293 - [c15]Han-Jia Ye, De-Chuan Zhan, Xue-Min Si, Yuan Jiang:
Learning Mahalanobis Distance Metric: Considering Instance Disturbance Helps. IJCAI 2017: 3315-3321 - 2016
- [c14]Han-Jia Ye, De-Chuan Zhan, Yuan Jiang:
Instance Specific Metric Subspace Learning: A Bayesian Approach. AAAI 2016: 2272-2278 - [c13]De-Chuan Zhan, Peng Hu, Zui Chu, Zhi-Hua Zhou:
Learning Expected Hitting Time Distance. AAAI 2016: 2309-2314 - [c12]Han-Jia Ye, De-Chuan Zhan, Xue-Min Si, Yuan Jiang:
Learning Feature Aware Metric. ACML 2016: 286-301 - [c11]Han-Jia Ye, De-Chuan Zhan, Xiaolin Li, Zhen-Chuan Huang, Yuan Jiang:
College Student Scholarships and Subsidies Granting: A Multi-modal Multi-label Approach. ICDM 2016: 559-568 - [c10]Yang Yang, De-Chuan Zhan, Yuan Jiang:
Learning by Actively Querying Strong Modal Features. IJCAI 2016: 2280-2286 - [c9]Han-Jia Ye, De-Chuan Zhan, Xue-Min Si, Yuan Jiang, Zhi-Hua Zhou:
What Makes Objects Similar: A Unified Multi-Metric Learning Approach. NIPS 2016: 1235-1243 - 2015
- [j4]Ju-Hua Hu, De-Chuan Zhan, Xintao Wu
Pairwised Specific Distance Learning from Physical Linkages. ACM Trans. Knowl. Discov. Data 9(3): 20:1-20:27 (2015) - [c8]Han-Jia Ye, De-Chuan Zhan, Yuan Miao, Yuan Jiang, Zhi-Hua Zhou:
Rank Consistency based Multi-View Learning: A Privacy-Preserving Approach. CIKM 2015: 991-1000 - [c7]Yang Yang, Han-Jia Ye, De-Chuan Zhan, Yuan Jiang:
Auxiliary Information Regularized Machine for Multiple Modality Feature Learning. IJCAI 2015: 1033-1039 - 2014
- [j3]Qing Zhang, Yilong Yin, De-Chuan Zhan, Jingliang Peng:
A Novel Serial Multimodal Biometrics Framework Based on Semisupervised Learning Techniques. IEEE Trans. Inf. Forensics Secur. 9(10): 1681-1694 (2014) - 2013
- [c6]Cam-Tu Nguyen, De-Chuan Zhan, Zhi-Hua Zhou:
Multi-Modal Image Annotation with Multi-Instance Multi-Label LDA. IJCAI 2013: 1558-1564 - [c5]Meng-Yuan Shi, De-Chuan Zhan:
Multi Gesture Recognition: A Tracking Learning Detection Approach. IScIDE 2013: 714-721 - 2012
- [c4]Qing Zhang, De-Chuan Zhan, Yilong Yin:
Learning with Weak Views Based on Dependence Maximization Dimensionality Reduction. IScIDE 2012: 557-564
2000 – 2009
- 2009
- [c3]De-Chuan Zhan, Ming Li, Yufeng Li, Zhi-Hua Zhou:
Learning instance specific distances using metric propagation. ICML 2009: 1225-1232 - 2007
- [j2]Yang Yu, De-Chuan Zhan, Xu-Ying Liu, Ming Li, Zhi-Hua Zhou:
Predicting Future Customers via Ensembling Gradually Expanded Trees. Int. J. Data Warehous. Min. 3(2): 12-21 (2007) - [c2]Zhi-Hua Zhou, De-Chuan Zhan, Qiang Yang:
Semi-Supervised Learning with Very Few Labeled Training Examples. AAAI 2007: 675-680 - 2006
- [c1]De-Chuan Zhan, Zhi-Hua Zhou:
Neighbor Line-Based Locally Linear Embedding. PAKDD 2006: 806-815 - 2005
- [j1]Xin Geng, De-Chuan Zhan, Zhi-Hua Zhou:
Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Trans. Syst. Man Cybern. Part B 35(6): 1098-1107 (2005)
Coauthor Index
[j33] [j32] [j30] [j29] [j27] [j25] [c90] [c89] [c88] [c84] [c83] [c82] [c78] [c76] [i78] [i76] [i75] [i71] [i70] [i69] [i64] [i63] [i62] [i60] [i59] [i56] [i55] [j24] [j21] [j20] [j19] [c74] [c72] [c71] [c70] [c69] [c67] [c66] [c63] [c62] [c60] [c59] [i53] [i52] [i51] [i50] [i49] [i46] [i45] [i41] [i40] [i39] [i37] [i36] [i35] [i34] [i32] [j16] [c57] [c55] [c54] [c52] [i31] [i29] [i28] [i27] [i26] [i25] [i24] [i22] [j13] [j12] [c46] [c45] [c44] [c43] [c39] [c38] [i19] [i18] [i17] [i16] [i14] [i13] [i10] [i7] [j8] [j7] [c35] [c34] [i6] [i5] [i4] [j6] [j5] [i2] [c24] [c22] [i1] [c15] [c14] [c12] [c11] [c9] [c8] [c7]
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