ZHANG XIN
👩🏻💻Profile
I am currently a Research Scientist at CFAR, A*STAR. Prior to that, I was a visiting student at A*STAR, working with Prof. Joey Tianyi Zhou and Dr. Jiawei Du.
I received my Ph.D. degree in December 2024 from Xidian University, under the supervision of Prof. Yunsong Li and Prof. Weiying Xie. Before that, I obtained my Bachelor’s degree in 2019, also from Xidian University.
My research interests lie in machine learning and computer vision, with a focus on data-centric efficiency, generative AI, and deepfake generation & detection.
⚡️News!
📌 [2025.10] I will be serving as the session chair for Generative AI: Generative Multimedia IV and VI in ACMMM 2025. I look forward to meeting you in Dublin 🇮🇪!
📌 [2025.9] Our work "Beyond Modality Collapse: Representations Blending for Multimodal Dataset Distillation" is accepted by NeurIPS 2025! See you in San Diego 🇺🇸. Paper Codes
📌 [2025.8] Our ICLR work *“Breaking Class Barriers”* was presented at the Workshop on Tensor Representation for Machine Learning(AIP, RIKEN 🇯🇵). Thanks to Prof. Qibin Zhao for the invitation!
📌 [2025.5] Our work "Beyond Modality Collapse: Representations Blending for Multimodal Dataset Distillation" is posted online! Paper
📌 [2025.03] Honored to receive the 2024 A*STAR Career Development Fund (CDF) award as PI. Sincere thanks to my collaborators and mentors for their continued support and guidance.News
📌 [2025.01] Our work "Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator" is accepted by ICLR! See you in 🇸🇬. Paper Code
📌 [2024.12] Successfully defended my dissertation and obtained my Ph.D. degree! 🎓
📌 [2024.11] Win National Scholarship for PhD Student again!
📌 [2024.10] Our dataset pruning work published on ACMM has been nominated for Best Paper Award!
📌 [2024.9] Our work "Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment" is accepted by **NeurIPS (spotlight)**! Paper
📌 [2024.8] Our work "Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator" is posted online! Paper
📌 [2024.7] Our work "Markov-PQ: Joint Pruning-Quantization via Learnable Markov Chain" is accepted by **IEEE TCSVT**! Paper
📌 [2024.7] Our work "Evolution-aware VAriance (EVA) Coreset Selection for Medical Image Classification" is accepted by **ACMM (oral, best paper nomination)**! Paper
📌 [2024.2] Our work "Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning" is accepted by **CVPR**! See you in 🇺🇸 Paper Codes
📌 [2023.10] Win National Scholarship for PhD Student!
📌 [2025.9] Our work "Beyond Modality Collapse: Representations Blending for Multimodal Dataset Distillation" is accepted by NeurIPS 2025! See you in San Diego 🇺🇸. Paper Codes
📌 [2025.8] Our ICLR work *“Breaking Class Barriers”* was presented at the Workshop on Tensor Representation for Machine Learning(AIP, RIKEN 🇯🇵). Thanks to Prof. Qibin Zhao for the invitation!
📌 [2025.5] Our work "Beyond Modality Collapse: Representations Blending for Multimodal Dataset Distillation" is posted online! Paper
📌 [2025.03] Honored to receive the 2024 A*STAR Career Development Fund (CDF) award as PI. Sincere thanks to my collaborators and mentors for their continued support and guidance.News
📌 [2025.01] Our work "Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator" is accepted by ICLR! See you in 🇸🇬. Paper Code
📌 [2024.12] Successfully defended my dissertation and obtained my Ph.D. degree! 🎓
📌 [2024.11] Win National Scholarship for PhD Student again!
📌 [2024.10] Our dataset pruning work published on ACMM has been nominated for Best Paper Award!
📌 [2024.9] Our work "Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment" is accepted by **NeurIPS (spotlight)**! Paper
📌 [2024.8] Our work "Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator" is posted online! Paper
📌 [2024.7] Our work "Markov-PQ: Joint Pruning-Quantization via Learnable Markov Chain" is accepted by **IEEE TCSVT**! Paper
📌 [2024.7] Our work "Evolution-aware VAriance (EVA) Coreset Selection for Medical Image Classification" is accepted by **ACMM (oral, best paper nomination)**! Paper
📌 [2024.2] Our work "Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning" is accepted by **CVPR**! See you in 🇺🇸 Paper Codes
📌 [2023.10] Win National Scholarship for PhD Student!
📝Publications
For the full list, please refer to my Google Scholar page: Google Scholar
Data-centric Efficiency
[NeurIPS 2025] Beyond Modality Collapse: Representations Blending for Multimodal Dataset Distillation.
Xin Zhang, Ziruo Zhang, Jiawei Du, Zuozhu Liu, Joey Tianyi Zhou.
Xin Zhang, Ziruo Zhang, Jiawei Du, Zuozhu Liu, Joey Tianyi Zhou.
[ICLR 2025] Breaking Class Barriers: Efficient Dataset Distillation via Inter-class Feature Compensator.
Xin Zhang, Jiawei Du, Ping Liu, Joey Tianyi Zhou.
Xin Zhang, Jiawei Du, Ping Liu, Joey Tianyi Zhou.
[NeurIPS 2024 Spotlight] Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment.
Jiawei Du, Xin Zhang, Juncheng Hu, Wenxin Huang, Joey Tianyi Zhou.
Jiawei Du, Xin Zhang, Juncheng Hu, Wenxin Huang, Joey Tianyi Zhou.
[ACM MM 2024 (Oral)] Evolution-aware VAriance (EVA) Coreset Selection for Medical Image Classification.
Yuxin Hong, Xiao Zhang, Xin Zhang, Joey Tianyi Zhou.
Yuxin Hong, Xiao Zhang, Xin Zhang, Joey Tianyi Zhou.
[CVPR 2024] Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning.
Xin Zhang, Jiawei Du, Yunsong Li, Weiying Xie, Joey Tianyi Zhou.
Xin Zhang, Jiawei Du, Yunsong Li, Weiying Xie, Joey Tianyi Zhou.
Model-centric Efficiency
[IEEE TNNLS 2023] Block-Wise Partner Learning for Model Compression.
Xin Zhang, Weiying Xie, Yunsong Li, Jie Lei, Kai Jiang, Leyuan Fang, Qian Du.
Xin Zhang, Weiying Xie, Yunsong Li, Jie Lei, Kai Jiang, Leyuan Fang, Qian Du.
[IEEE TIP 2023] Reaf: Remembering enhancement and entropy-based asymptotic forgetting for filter pruning.
Xin Zhang, Weiying Xie, Yunsong Li, Kai Jiang, Leyuan Fang.
Xin Zhang, Weiying Xie, Yunsong Li, Kai Jiang, Leyuan Fang.
[IEEE TGRS 2023] Co-compression via superior gene for remote sensing scene classification.
Weiying Xie, Xiaoyi Fan, Xin Zhang, Yunsong Li, Min Sheng, Leyuan Fang.
Weiying Xie, Xiaoyi Fan, Xin Zhang, Yunsong Li, Min Sheng, Leyuan Fang.
[IEEE TC 2021] Filter pruning via learned representation median in the frequency domain.
Xin Zhang, Weiying Xie, Yunsong Li, Jie Lei, Qian Du.
Xin Zhang, Weiying Xie, Yunsong Li, Jie Lei, Qian Du.
Image Processing
[IEEE TC 2021] Weakly supervised low-rank representation for hyperspectral anomaly detection.
Weiying Xie, Xin Zhang, Yunsong Li, Jie Lei, Jiaojiao Li, Qian Du.
Weiying Xie, Xin Zhang, Yunsong Li, Jie Lei, Jiaojiao Li, Qian Du.
[IEEE Jstar 2020] Background learning based on target suppression constraint for hyperspectral target detection.
Weiying Xie, Xin Zhang, Yunsong Li, Keyan Wang, Qian Du.
Weiying Xie, Xin Zhang, Yunsong Li, Keyan Wang, Qian Du.