Cover Image

Fair4Free: Generating High-fidelity Fair Synthetic Samples using Data Free Distillation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sikder, Md Fahim, de Leng, Daniel, Heintz, Fredrik
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Generating Synthetic Fair Syntax-agnostic Data by Learning and Distilling Fair Representation
by: Sikder, Md Fahim, et al.
Published: (2024)

FairX: A comprehensive benchmarking tool for model analysis using fairness, utility, and explainability
by: Sikder, Md Fahim, et al.
Published: (2024)

Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative Adversarial Networks
by: Ramachandranpillai, Resmi, et al.
Published: (2024)

TransFusion: Generating Long, High Fidelity Time Series using Diffusion Models with Transformers
by: Sikder, Md Fahim, et al.
Published: (2023)

FairTabGen: High-Fidelity and Fair Synthetic Health Data Generation from Limited Samples
by: Nagesh, Nitish, et al.
Published: (2025)

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation
by: Nagesh, Nitish, et al.
Published: (2025)

Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study
by: Panagiotou, Emmanouil, et al.
Published: (2024)

Fair Class-Incremental Learning using Sample Weighting
by: Park, Jaeyoung, et al.
Published: (2024)

LMS-AutoTSF: Learnable Multi-Scale Decomposition and Integrated Autocorrelation for Time Series Forecasting
by: Delibasoglu, Ibrahim, et al.
Published: (2024)

EDformer: Embedded Decomposition Transformer for Interpretable Multivariate Time Series Predictions
by: Chakraborty, Sanjay, et al.
Published: (2024)

Advancing Diffusion Models: Alias-Free Resampling and Enhanced Rotational Equivariance
by: Anjum, Md Fahim
Published: (2024)

Trusting Fair Data: Leveraging Quality in Fairness-Driven Data Removal Techniques
by: Duong, Manh Khoi, et al.
Published: (2024)

Quantitative Auditing of AI Fairness with Differentially Private Synthetic Data
by: Yuan, Chih-Cheng Rex, et al.
Published: (2025)

FairDD: Fair Dataset Distillation
by: Zhou, Qihang, et al.
Published: (2024)

Fairness under Covariate Shift: Improving Fairness-Accuracy tradeoff with few Unlabeled Test Samples
by: Havaldar, Shreyas, et al.
Published: (2023)

Fair Overlap Number of Balls (Fair-ONB): A Data-Morphology-based Undersampling Method for Bias Reduction
by: Pascual-Triana, José Daniel, et al.
Published: (2024)

Robust and Resource-Efficient Data-Free Knowledge Distillation by Generative Pseudo Replay
by: Binici, Kuluhan, et al.
Published: (2022)

Fair Dataset Distillation via Cross-Group Barycenter Alignment
by: Moslemi, Mohammad Hossein, et al.
Published: (2026)

FairSAM: Fair Classification on Corrupted Data Through Sharpness-Aware Minimization
by: Dai, Yucong, et al.
Published: (2025)

Continuous Fair SMOTE -- Fairness-Aware Stream Learning from Imbalanced Data
by: Lammers, Kathrin, et al.
Published: (2025)

TABFAIRGDT: A Fast Fair Tabular Data Generator using Autoregressive Decision Trees
by: Panagiotou, Emmanouil, et al.
Published: (2025)

FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation
by: Heilmann, Xenia, et al.
Published: (2025)

Decomposed Trust: Privacy, Adversarial Robustness, Ethics, and Fairness in Low-Rank LLMs
by: Asante, Daniel Agyei, et al.
Published: (2025)

C2G-KD: PCA-Constrained Generator for Data-Free Knowledge Distillation
by: Bengtsson, Magnus, et al.
Published: (2025)

ProxiMix: Enhancing Fairness with Proximity Samples in Subgroups
by: Hu, Jingyu, et al.
Published: (2024)

FairSample: Training Fair and Accurate Graph Convolutional Neural Networks Efficiently
by: Cong, Zicun, et al.
Published: (2024)

LiPCoT: Linear Predictive Coding based Tokenizer for Self-supervised Learning of Time Series Data via Language Models
by: Anjum, Md Fahim
Published: (2024)

Understanding Fairness Surrogate Functions in Algorithmic Fairness
by: Yao, Wei, et al.
Published: (2023)

Fair-FLIP: Fair Deepfake Detection with Fairness-Oriented Final Layer Input Prioritising
by: Szandala, Tomasz, et al.
Published: (2025)

Synthetic Data Generation for Augmenting Small Samples
by: Liu, Dan, et al.
Published: (2025)

Fairness Without Harm: An Influence-Guided Active Sampling Approach
by: Pang, Jinlong, et al.
Published: (2024)

IFFair: Influence Function-driven Sample Reweighting for Fair Classification
by: Yang, Jingran, et al.
Published: (2025)

Improving Group Fairness in Knowledge Distillation via Laplace Approximation of Early Exits
by: Fasth, Edvin, et al.
Published: (2025)

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data
by: Cai, Daniel, et al.
Published: (2025)

Post-Pruning Accuracy Recovery via Data-Free Knowledge Distillation
by: Tripurwar, Chinmay, et al.
Published: (2025)

FairSHAP: Preprocessing for Fairness Through Attribution-Based Data Augmentation
by: Zhu, Lin, et al.
Published: (2025)

ComFairGNN: Community Fair Graph Neural Network
by: Sium, Yonas, et al.
Published: (2024)

FairGU: Fairness-aware Graph Unlearning in Social Networks
by: Luo, Renqiang, et al.
Published: (2026)

Federated Unlearning in the Wild: Rethinking Fairness and Data Discrepancy
by: Huang, ZiHeng, et al.
Published: (2025)

Reproducibility study of FairAC
by: de Jong, Gijs, et al.
Published: (2024)