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2020 – today
- 2024
[j16]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)- [j15]Han-Jia Ye
, Da-Wei Zhou, Lanqing Hong, Zhenguo Li, Xiu-Shen Wei, De-Chuan Zhan:
Contextualizing Meta-Learning via Learning to Decompose. IEEE Trans. Pattern Anal. Mach. Intell. 46(1): 117-133 (2024) - [j14]Han-Jia Ye, Lu Ming, De-Chuan Zhan, Wei-Lun Chao:
Few-Shot Learning With a Strong Teacher. IEEE Trans. Pattern Anal. Mach. Intell. 46(3): 1425-1440 (2024) - [j13]Lu Han, Han-Jia Ye, De-Chuan Zhan:
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) - [c46]Yu-Cheng He, Yao-Xiang Ding, Han-Jia Ye, Zhi-Hua Zhou:
Learning Only When It Matters: Cost-Aware Long-Tailed Classification. AAAI 2024: 12411-12420 - [c45]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 - [c44]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 - [c43]Chao Yi, Lu Ren, De-Chuan Zhan, Han-Jia Ye:
Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification. CVPR 2024: 27392-27401 - [c42]Lu Han, Han-Jia Ye, De-Chuan Zhan:
SIN: Selective and Interpretable Normalization for Long-Term Time Series Forecasting. ICML 2024 - [c41]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 - [c40]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 - [c39]Bowen Zheng, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Multi-layer Rehearsal Feature Augmentation for Class-Incremental Learning. ICML 2024 - [c38]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 - [c37]Yucheng Wu, Leye Wang, Xiao Han, Han-Jia Ye:
Graph Contrastive Learning with Cohesive Subgraph Awareness. WWW 2024: 629-640 - [i56]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) - [i55]Yucheng Wu, Leye Wang, Xiao Han, Han-Jia Ye:
Graph Contrastive Learning with Cohesive Subgraph Awareness. CoRR abs/2401.17580 (2024) - [i54]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) - [i53]Chao Yi, De-Chuan Zhan, Han-Jia Ye:
Bridge the Modality and Capacity Gaps in Vision-Language Model Selection. CoRR abs/2403.13797 (2024) - [i52]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) - [i51]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) - [i50]Chao Yi, Lu Ren, De-Chuan Zhan, Han-Jia Ye:
Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification. CoRR abs/2404.17753 (2024) - [i49]Shiyin Lu, Yang Li, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, Han-Jia Ye:
Ovis: Structural Embedding Alignment for Multimodal Large Language Model. CoRR abs/2405.20797 (2024) - [i48]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) - [i47]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) - [i46]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) - [i45]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) - [i44]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) - [i43]Si-Yang Liu, Hao-Run Cai, Qi-Le Zhou, Han-Jia Ye:
TALENT: A Tabular Analytics and Learning Toolbox. CoRR abs/2407.04057 (2024) - [i42]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) - [i41]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
- [j12]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) - [j11]Han-Jia Ye, Su Lu, De-Chuan Zhan:
Generalized Knowledge Distillation via Relationship Matching. IEEE Trans. Pattern Anal. Mach. Intell. 45(2): 1817-1834 (2023) - [j10]Han-Jia Ye, Lu Han, De-Chuan Zhan:
Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks. IEEE Trans. Pattern Anal. Mach. Intell. 45(3): 3721-3737 (2023) - [j9]Da-Wei Zhou, Han-Jia Ye, Liang Ma, Di Xie, Shiliang Pu, De-Chuan Zhan:
Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks. IEEE Trans. Pattern Anal. Mach. Intell. 45(11): 12816-12831 (2023) - [c36]Ting-Ji Huang, Qi-Le Zhou, Han-Jia Ye, De-Chuan Zhan:
Change Point Detection via Synthetic Signals. AALTD@ECML/PKDD 2023: 25-35 - [c35]Yi-Kai Zhang, Qi-Wei Wang, De-Chuan Zhan, Han-Jia Ye:
Learning Debiased Representations via Conditional Attribute Interpolation. CVPR 2023: 7599-7608 - [c34]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 - [c33]Lu Han, Han-Jia Ye, De-Chuan Zhan:
Augmentation Component Analysis: Modeling Similarity via the Augmentation Overlaps. ICLR 2023 - [c32]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 - [c31]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 - [c30]Bowen Zheng, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Preserving Locality in Vision Transformers for Class Incremental Learning. ICME 2023: 1157-1162 - [c29]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 - [c28]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 - [c27]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 - [c26]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 - [i40]Lu Han, Han-Jia Ye, De-Chuan Zhan:
On Pseudo-Labeling for Class-Mismatch Semi-Supervised Learning. CoRR abs/2301.06010 (2023) - [i39]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) - [i38]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) - [i37]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) - [i36]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) - [i35]Fu-Yun Wang, Wenshuo Chen, Guanglu Song, Han-Jia Ye, Yu Liu, Hongsheng Li:
Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising. CoRR abs/2305.18264 (2023) - [i34]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) - [i33]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) - [i32]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) - [i31]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) - [i30]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) - [i29]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) - [i28]Han-Jia Ye, Qi-Le Zhou, De-Chuan Zhan:
Training-Free Generalization on Heterogeneous Tabular Data via Meta-Representation. CoRR abs/2311.00055 (2023) - [i27]Lu Han, Xu-Yang Chen, Han-Jia Ye, De-Chuan Zhan:
Learning Robust Precipitation Forecaster by Temporal Frame Interpolation. CoRR abs/2311.18341 (2023) - [i26]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) - [i25]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
- [j8]Lu Han, Han-Jia Ye, De-Chuan Zhan:
On Pseudo-Labeling for Class-Mismatch Semi-Supervised Learning. Trans. Mach. Learn. Res. 2022 (2022) - [c25]Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, Liang Ma, Shiliang Pu, De-Chuan Zhan:
Forward Compatible Few-Shot Class-Incremental Learning. CVPR 2022: 9036-9046 - [c24]Han-Jia Ye, Yi Shi, De-Chuan Zhan:
Identifying Ambiguous Similarity Conditions via Semantic Matching. CVPR 2022: 16589-16598 - [c23]Fu-Yun Wang, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
FOSTER: Feature Boosting and Compression for Class-Incremental Learning. ECCV (25) 2022: 398-414 - [c22]Han-Jia Ye, Wei-Lun Chao:
How to Train Your MAML to Excel in Few-Shot Classification. ICLR 2022 - [c21]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 - [i24]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) - [i23]Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks. CoRR abs/2203.17030 (2022) - [i22]Han-Jia Ye, Yi Shi, De-Chuan Zhan:
Identifying Ambiguous Similarity Conditions via Semantic Matching. CoRR abs/2204.04053 (2022) - [i21]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) - [i20]Su Lu, Han-Jia Ye, De-Chuan Zhan:
Faculty Distillation with Optimal Transport. CoRR abs/2204.11526 (2022) - [i19]Han-Jia Ye, Su Lu, De-Chuan Zhan:
Generalized Knowledge Distillation via Relationship Matching. CoRR abs/2205.01915 (2022) - [i18]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) - [i17]Lu Han, Han-Jia Ye, De-Chuan Zhan:
Contrastive Principal Component Learning: Modeling Similarity by Augmentation Overlap. CoRR abs/2206.00471 (2022) - 2021
- [j7]Han-Jia Ye, Hexiang Hu, De-Chuan Zhan:
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning. Int. J. Comput. Vis. 129(6): 1930-1953 (2021) - [j6]Han-Jia Ye, De-Chuan Zhan, Yuan Jiang, Zhi-Hua Zhou:
Heterogeneous Few-Shot Model Rectification With Semantic Mapping. IEEE Trans. Pattern Anal. Mach. Intell. 43(11): 3878-3891 (2021) - [j5]Xiu-Shen Wei, Han-Jia Ye, Xin Mu, Jianxin Wu, Chunhua Shen, Zhi-Hua Zhou:
Multi-Instance Learning With Emerging Novel Class. IEEE Trans. Knowl. Data Eng. 33(5): 2109-2120 (2021) - [c20]Haoyue Bai, Rui Sun, Lanqing Hong, Fengwei Zhou, Nanyang Ye, Han-Jia Ye, S.-H. Gary Chan, Zhenguo Li:
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation. AAAI 2021: 6705-6713 - [c19]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 - [c18]Han-Jia Ye, Xin-Chun Li, De-Chuan Zhan:
Task Cooperation for Semi-Supervised Few-Shot Learning. AAAI 2021: 10682-10690 - [c17]Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Learning Placeholders for Open-Set Recognition. CVPR 2021: 4401-4410 - [c16]Han-Jia Ye, De-Chuan Zhan, Wei-Lun Chao:
Procrustean Training for Imbalanced Deep Learning. ICCV 2021: 92-102 - [c15]Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Co-Transport for Class-Incremental Learning. ACM Multimedia 2021: 1645-1654 - [c14]Xiu-Shen Wei, Jufeng Yang, Han-Jia Ye, Jian Yang:
MULL'21: First International Workshop on Multimedia Understanding with Less Labeling. ACM Multimedia 2021: 5704-5705 - [c13]Xiu-Shen Wei, Yang Shen, Xuhao Sun, Han-Jia Ye, Jian Yang:
A$^2$-Net: Learning Attribute-Aware Hash Codes for Large-Scale Fine-Grained Image Retrieval. NeurIPS 2021: 5720-5730 - [c12]Su Lu, Han-Jia Ye, Le Gan, De-Chuan Zhan:
Towards Enabling Meta-Learning from Target Models. NeurIPS 2021: 8060-8071 - [e1]Xiu-Shen Wei, Han-Jia Ye, Jufeng Yang, Jian Yang:
MULL'21: Multimedia Understanding with Less Labeling on Multimedia Understanding with Less Labeling, Virtual Event, China, 24 October 2021. ACM 2021, ISBN 978-1-4503-8681-4 [contents] - [i16]Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Learning Placeholders for Open-Set Recognition. CoRR abs/2103.15086 (2021) - [i15]Han-Jia Ye, De-Chuan Zhan, Wei-Lun Chao:
Procrustean Training for Imbalanced Deep Learning. CoRR abs/2104.01769 (2021) - [i14]Su Lu, Han-Jia Ye, De-Chuan Zhan:
Support-Target Protocol for Meta-Learning. CoRR abs/2104.03736 (2021) - [i13]Su Lu, Han-Jia Ye, De-Chuan Zhan:
Few-Shot Action Recognition with Compromised Metric via Optimal Transport. CoRR abs/2104.03737 (2021) - [i12]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) - [i11]Han-Jia Ye, Wei-Lun Chao:
How to Train Your MAML to Excel in Few-Shot Classification. CoRR abs/2106.16245 (2021) - [i10]Han-Jia Ye, Lu Ming, De-Chuan Zhan, Wei-Lun Chao:
Few-Shot Learning with a Strong Teacher. CoRR abs/2107.00197 (2021) - [i9]Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Co-Transport for Class-Incremental Learning. CoRR abs/2107.12654 (2021) - [i8]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
- [j4]Han-Jia Ye, Xiang-Rong Sheng, De-Chuan Zhan:
Few-shot learning with adaptively initialized task optimizer: a practical meta-learning approach. Mach. Learn. 109(3): 643-664 (2020) - [j3]Han-Jia Ye, De-Chuan Zhan, Nan Li, Yuan Jiang:
Learning Multiple Local Metrics: Global Consideration Helps. IEEE Trans. Pattern Anal. Mach. Intell. 42(7): 1698-1712 (2020) - [c11]Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, Fei Sha:
Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions. CVPR 2020: 8805-8814 - [c10]Han-Jia Ye, Su Lu, De-Chuan Zhan:
Distilling Cross-Task Knowledge via Relationship Matching. CVPR 2020: 12393-12402 - [i7]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) - [i6]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) - [i5]Chao Wang, Ruo-Ze Liu, Han-Jia Ye, Yang Yu:
Novelty-Prepared Few-Shot Classification. CoRR abs/2003.00497 (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]Haoyue Bai, Rui Sun, Lanqing Hong, Fengwei Zhou, Nanyang Ye, Han-Jia Ye, S.-H. Gary Chan, Zhenguo Li:
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation. CoRR abs/2012.09382 (2020)
2010 – 2019
- 2019
- [j2]Han-Jia Ye, De-Chuan Zhan, Yuan Jiang:
Fast generalization rates for distance metric learning. Mach. Learn. 108(2): 267-295 (2019) - [j1]Han-Jia Ye, De-Chuan Zhan, Yuan Jiang, Zhi-Hua Zhou:
What Makes Objects Similar: A Unified Multi-Metric Learning Approach. IEEE Trans. Pattern Anal. Mach. Intell. 41(5): 1257-1270 (2019) - [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
- [c9]Han-Jia Ye, De-Chuan Zhan, Yuan Jiang, Zhi-Hua Zhou:
Rectify Heterogeneous Models with Semantic Mapping. ICML 2018: 1904-1913 - [c8]Han-Jia Ye, Xiang-Rong Sheng, De-Chuan Zhan, Peng He:
Distance Metric Facilitated Transportation between Heterogeneous Domains. IJCAI 2018: 3012-3018 - [i1]Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, Fei Sha:
Learning Embedding Adaptation for Few-Shot Learning. CoRR abs/1812.03664 (2018) - 2017
- [c7]Han-Jia Ye, De-Chuan Zhan, Xue-Min Si, Yuan Jiang:
Learning Mahalanobis Distance Metric: Considering Instance Disturbance Helps. IJCAI 2017: 3315-3321 - 2016
- [c6]Han-Jia Ye, De-Chuan Zhan, Yuan Jiang:
Instance Specific Metric Subspace Learning: A Bayesian Approach. AAAI 2016: 2272-2278 - [c5]Han-Jia Ye, De-Chuan Zhan, Xue-Min Si, Yuan Jiang:
Learning Feature Aware Metric. ACML 2016: 286-301 - [c4]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 - [c3]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
- [c2]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 - [c1]Yang Yang, Han-Jia Ye, De-Chuan Zhan, Yuan Jiang:
Auxiliary Information Regularized Machine for Multiple Modality Feature Learning. IJCAI 2015: 1033-1039
Coauthor Index
[j16] [j15] [j14] [j13] [c45] [c44] [c43] [c42] [c41] [c40] [c39] [c38] [i56] [i54] [i53] [i52] [i51] [i50] [i48] [i47] [i46] [i45] [i44] [i42] [i41] [j12] [j11] [j10] [j9] [c36] [c35] [c34] [c33] [c32] [c31] [c30] [c29] [c28] [c27] [c26] [i40] [i39] [i38] [i37] [i36] [i34] [i33] [i32] [i31] [i30] [i29] [i28] [i27] [i26] [i25] [j8] [c25] [c24] [c23] [c21] [i24] [i23] [i22] [i21] [i20] [i19] [i18] [i17] [j7] [j6] [c19] [c18] [c17] [c16] [c15] [c12] [i16] [i15] [i14] [i13] [i12] [i10] [i9] [i8] [j4] [j3] [c11] [c10] [i7] [i6] [i4] [j2] [j1] [i2] [c9] [c8] [i1] [c7] [c6] [c5] [c4] [c3] [c2] [c1]
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last updated on 2024-11-11 22:23 CET by the dblp team
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