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!

📌 [2025.03] 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.
📌 [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

[ICLR 2025] Breaking Class Barriers: Efficient Dataset Distillation via Inter-class Feature Compensator.
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.

[ACM MM 2024 (Oral)] Evolution-aware VAriance (EVA) Coreset Selection for Medical Image Classification.
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.

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.

[IEEE TIP 2023] Reaf: Remembering enhancement and entropy-based asymptotic forgetting for filter pruning.
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.

[IEEE TC 2021] Filter pruning via learned representation median in the frequency domain.
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.

[IEEE Jstar 2020] Background learning based on target suppression constraint for hyperspectral target detection.
Weiying Xie, Xin Zhang, Yunsong Li, Keyan Wang, Qian Du.