Synthetic Data Selection

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5 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Hard, Yet Reducible: Controlled Forward Transfer for Synthetic Degradation Curation
Oct 7, 2026Chunming He, Kailai Zhou, Jiaming Zuo +6Training Data Curation Synthetic Data Selection
Tsinghua University, China · X3000 Inspection Co., Ltd · AI4X Team, Nanyang Technological University, Singapore +1
Concentration, Not Uncertainty: Why Targeted Synthetic Data Doesn't Help Camouflaged Object Detection
Oct 7, 2026Akshat Dobhal, Sanjay SinghSynthetic Data Selection Synthetic Data Generation
Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
Training-Aware Target Coverage for Synthetic Data Selection
Sep 30, 2026Yang Ba, Michelle V. Mancenido, Rong PanLLM Fine-Tuning Data Selection
School of Computing and Augmented Intelligence, Arizona State University · School of Mathematical and Natural Sciences, Arizona State University
Effective Synthetic Data Curation Requires Group-Level Signals
Sep 30, 2026Cathy Jiao, Chenyan XiongSynthetic Data Pretraining Training Data Curation
Language Technologies Institute, Carnegie Mellon University
Source Identification Is Not Fitness Testing: Measuring the Limits of Synthetic-Data Attribution
Sep 30, 2026Joss ArmstrongModel Collapse Training Data Attribution
Ericsson, Athlone, Ireland
Synthetic Data Characterization via Training Dynamics
Sep 30, 2026Irene Lago, Ana Ezquerro, David VilaresSynthetic Data Generation for Language Models Synthetic Data Evaluation
Universidade da Coruña, CITIC, Spain · Graz University of Technology, IML, Austria
Let Training Guide Selection: Online Synthetic Data Filtering via Real-Anchored Utility
Sep 24, 2026Yanran Wu, Sana Lakdawala, Renzo Tassara Miller +6Data Valuation Synthetic Data Selection
Purdue University · Work done at Meta. · Meta
Epiplexity Guided Data Selection and Generation for Out-of-Distribution Generalization
Aug 12, 2026Ellen Su, Andres Potapczynski, Shikai Qiu +2Synthetic Data Generation OOD Generalization
New York University · Inherent
K-IPO: Kendall-constrained Importance Preserving Oversampling for Imbalanced Tabular Data
Jul 17, 2026Marios Tyrovolas, Argiris Sofotasios, Dimitris Metaxakis +4Class-Imbalanced Learning Synthetic Data Augmentation
Department of Informatics and Telecommunications, University of Ioannina, Arta, Greece · Industrial Systems Institute, Athena Research Center, Patras, Greece · Archimedes Unit, Athena Research Center, Athens, Greece +3
RUBRIC: Realism--Utility Balanced Ranking for Imbalanced Classification
Jul 10, 2026Yanxuan Yu, Dong Liu, Shu Wang +7Class-Imbalanced Learning Synthetic Data Augmentation
Columbia University · University of California, Los Angeles · University of Illinois Urbana-Champaign +3
Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting
Jul 2, 2026Disheng Liu, Tuo Liang, Chaoda Song +1Synthetic Data Augmentation Training Data Selection
Department of Computer and Data Sciences Case Western Reserve University Cleveland, OH, USA
A Filtered Mixture-of-Generators for Fully Synthetic Survival Training
Jun 30, 2026Niccolò Maria Rizzi, Eugenio Lomurno, Alberto Archetti +1Survival Analysis Synthetic Data Generation
Politecnico di Milano, Milan, Italy
Training-Free Metrics for Synthetic Object Detection Data: A Proxy for Detector Performance
Jun 18, 2026Myeongseok Nam, Donghun Yeo, Seungwook KimDistribution Matching Object Detection
GenGenAI, South Korea
Efficient Financial Language Understanding via Distillation with Synthetic Data
Jun 17, 2026Wen-Fong, Huang, Edwin SimpsonFinancial Sentiment Analysis Language Model Distillation
Xavier · School of Engineering Mathematics and Technology, University of Bristol
Causal-Privacy Audit Workflow for Synthetic and Distilled Data in Dropout Support
Jun 14, 2026Hanghang Zheng, Xiwei Zhuang, Zhong Wang +4Privacy Auditing Causal Effect Estimation
Central University of Finance and Economics, Beijing, PR China · China Development Bank, Beijing, PR China
When Sample Selection Bias Precipitates Model Collapse
Jun 11, 2026Xinbao Qiao, Xianglong Du, Wei Liu +4Synthetic Data Pretraining Model Collapse
National University of Singapore (Chongqing) Research Institute · Chongqing Key Laboratory of Trusted Perception and Interaction Technology for Intelligent and Connected Vehicles, Chongqing, China · The Chinese University of Hong Kong +4
Provenance-Grounded Gating and Adaptive Recovery in Synthetic Post-Training Data Curation
Jun 9, 2026Soham Bhattacharjee, Karun Sharma, Vinay Kumar Sankarapu +1Adaptive Sampling LLM Post-Training
Lexsi Labs
Pool-Select-Refine for Allocation-Aware Generative Dataset Distillation
Jun 1, 2026Wenmin Li, Shunsuke Sakai, Zhongkai Zhao +1Dataset Distillation Knowledge Distillation
Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui, 910-8507, Fukui, Japan · College of Computer Science and Artificial Intelligence, Southwest Minzu University, No.168, Wenxing Section, Dajian Road, Konggang Development Zone, Shuangliu District, Chengdu, 610225, Sichuan, China
On the Difficulty of Learning a Meta-network for Training Data Selection
May 30, 2026Zilin Du, Junqi Zhao, Boyang Albert LiMeta-Learning Training Data Selection
College of Computing and Data Science, Nanyang Technological University, Singapore.
LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection
May 11, 2026Abhishek Moturu, Anna Goldenberg, Babak TaatiSynthetic Data Augmentation Training Data Selection
Department of Computer Science University of Toronto The Hospital for Sick Children UHN KITE Research Institute T-CAIREM Vector Institute · Department of Computer Science Department of Laboratory Medicine and Pathobiology University of Toronto The Hospital for Sick Children T-CAIREM Vector Institute · Department of Computer Science Institute of Biomedical Engineering University of Toronto Rehabilitation Sciences Institute UHN KITE Research Institute Vector Institute
EvoSelect: Data-Efficient LLM Evolution for Targeted Task Adaptation
Apr 28, 2026Ting-Wei Li, Sirui Chen, Jiaru Zou +4LLM Fine-Tuning Synthetic Data Generation
University of Illinois Urbana-Champaign
QAQ: Bidirectional Semantic Coherence for Selecting High-Quality Synthetic Code Instructions
Mar 12, 2026Jiayin Lei, Ming Ma, Yunxi Duan +2Instruction-Tuning Data Selection Code Generation
Beijing University of Technology, Beijing, 100124, China · Institute of Neuroscience, State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China · University of Chinese Academy of Sciences, Beijing, 100049, China +1
SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection
Jan 6, 2026Zhiyong Cao, Dunqiang Liu, Qi Dai +9Synthetic Data Generation Conversational Agents
Zhaopin Limited · Institute of Automation, Chinese Academy of Sciences · School of Automation,Beijing Institute of Technology +3
Interpretable Similarity of Synthetic Image Utility
Dec 18, 2025Panagiota Gatoula, George Dimas, Dimitris K. IakovidisMedical Image Synthesis Synthetic Data Evaluation
University of Thessaly, Department of Computer Science and Biomedical Informatics, Lamia 35131 Greece

Oct 7, 2026, cs.CVJ/K move · Enter open · S save

Hard, Yet Reducible: Controlled Forward Transfer for Synthetic Degradation Curation

Chunming He, Kailai Zhou, Jiaming Zuo, Hanqi Liu+5

Tsinghua University, China · X3000 Inspection Co., Ltd · AI4X Team, Nanyang Technological University, Singapore+1

Training Data Curation Synthetic Data Selection Synthetic Data Augmentation

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Selecting synthetic degradations for dense prediction requires an estimate of their training utility, the generalization gain they bring under a finite training budget. Clean and degraded twins share content and labels, suggesting a score based on how much short training reduces the excess error caused by degradation. However, this gap can also shrink when clean performance deteriorates. Measuring the improvement on degraded images alone avoids that confound, but it still credits progress that the same amount of clean training would have produced. We propose the \textbf{controlled Reducible Degradation Gap} (cRDG) for regions defined by degradation type and severity. From a common checkpoint, cRDG runs two budget-matched probes that differ only in one augmentation slot, which holds either a synthetic degradation or a clean augmentation. The score is the gain on held-out degraded images relative to the clean-control probe. Clean harm is a separate feasibility constraint. cRDG reveals a correctable severity band in which training on the degradation yields high controlled gain under the available budget, and the band moves with the predictor, the starting checkpoint, and the training budget. \textbf{Curation of Reducible Bands} (\method) uses cRDG to select synthetic data without changing the predictor. On semantic segmentation and salient object detection, \method{} improves representative predictors under matched synthetic-data budgets and training schedules, extends to existing data-generation pipelines, and preserves clean performance. Code and supporting materials will be publicly released.

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