Generating Synthetic Fair Syntax-agnostic Data by Learning and Distilling Fair Representation

• URL: http://arxiv.org/abs/2408.10755v1
• Date: Tue, 20 Aug 2024 11:37:52 GMT
• Title: Generating Synthetic Fair Syntax-agnostic Data by Learning and Distilling Fair Representation
• Authors: Md Fahim Sikder, Resmi Ramachandranpillai, Daniel de Leng, Fredrik Heintz,
• Abstract summary: Existing bias-mitigating generative methods need in-processing fairness objectives and fail to consider computational overhead. We present a fair data generation technique based on knowledge distillation, where we use a small architecture to distill the fair representation in the latent space. Our approaches show a 5%, 5% and 10% rise in performance in fairness, synthetic sample quality and data utility, respectively, than the state-of-the-art fair generative model.
• Score: 4.1942958779358674
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data Fairness is a crucial topic due to the recent wide usage of AI powered applications. Most of the real-world data is filled with human or machine biases and when those data are being used to train AI models, there is a chance that the model will reflect the bias in the training data. Existing bias-mitigating generative methods based on GANs, Diffusion models need in-processing fairness objectives and fail to consider computational overhead while choosing computationally-heavy architectures, which may lead to high computational demands, instability and poor optimization performance. To mitigate this issue, in this work, we present a fair data generation technique based on knowledge distillation, where we use a small architecture to distill the fair representation in the latent space. The idea of fair latent space distillation enables more flexible and stable training of Fair Generative Models (FGMs). We first learn a syntax-agnostic (for any data type) fair representation of the data, followed by distillation in the latent space into a smaller model. After distillation, we use the distilled fair latent space to generate high-fidelity fair synthetic data. While distilling, we employ quality loss (for fair distillation) and utility loss (for data utility) to ensure that the fairness and data utility characteristics remain in the distilled latent space. Our approaches show a 5%, 5% and 10% rise in performance in fairness, synthetic sample quality and data utility, respectively, than the state-of-the-art fair generative model.

Related papers

• Data Augmentation via Diffusion Model to Enhance AI Fairness [1.2979015577834876]
  This paper explores the potential of diffusion models to generate synthetic data to improve AI fairness. The Tabular Denoising Diffusion Probabilistic Model (Tab-DDPM) was utilized with different amounts of generated data for data augmentation. Experimental results demonstrate that the synthetic data generated by Tab-DDPM improves fairness in binary classification.
  arXiv  Detail & Related papers  (2024-10-20T18:52:31Z)
• Fair4Free: Generating High-fidelity Fair Synthetic Samples using Data Free Distillation [4.915744683251151]
  This work presents a novel generative model to generate synthetic fair data using data-free distillation in the latent space. In our approach, we first train a teacher model to create fair representation and then distil the knowledge to a student model. The process of distilling the student model is data-free, i.e. the student model does not have access to the training dataset while distilling.
  arXiv  Detail & Related papers  (2024-10-02T11:16:11Z)
• Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data [2.6016285265085526]
  Student models show a significant drop in accuracy compared to models trained on real data. By training these layers using either real or synthetic data, we reveal that the drop mainly stems from the model's final layers. Our results suggest an improved trade-off between the amount of real training data used and the model's accuracy.
  arXiv  Detail & Related papers  (2024-05-06T07:51:13Z)
• Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents [43.282328554697564]
  We propose a highly efficient FL dataset distillation framework on the server side. Unlike previous strategies, our technique enables the server to leverage prior knowledge from pre-trained deep generative models. Our framework converges faster than the baselines because rather than the server trains on several sets of heterogeneous data distributions, it trains on a multi-modal distribution.
  arXiv  Detail & Related papers  (2023-12-03T23:30:48Z)
• On the Stability of Iterative Retraining of Generative Models on their own Data [56.153542044045224]
  We study the impact of training generative models on mixed datasets. We first prove the stability of iterative training under the condition that the initial generative models approximate the data distribution well enough. We empirically validate our theory on both synthetic and natural images by iteratively training normalizing flows and state-of-the-art diffusion models.
  arXiv  Detail & Related papers  (2023-09-30T16:41:04Z)
• Distill Gold from Massive Ores: Bi-level Data Pruning towards Efficient Dataset Distillation [96.92250565207017]
  We study the data efficiency and selection for the dataset distillation task. By re-formulating the dynamics of distillation, we provide insight into the inherent redundancy in the real dataset. We find the most contributing samples based on their causal effects on the distillation.
  arXiv  Detail & Related papers  (2023-05-28T06:53:41Z)
• Synthetic data, real errors: how (not) to publish and use synthetic data [86.65594304109567]
  We show how the generative process affects the downstream ML task. We introduce Deep Generative Ensemble (DGE) to approximate the posterior distribution over the generative process model parameters.
  arXiv  Detail & Related papers  (2023-05-16T07:30:29Z)
• Non-Invasive Fairness in Learning through the Lens of Data Drift [88.37640805363317]
  We show how to improve the fairness of Machine Learning models without altering the data or the learning algorithm. We use a simple but key insight: the divergence of trends between different populations, and, consecutively, between a learned model and minority populations, is analogous to data drift. We explore two strategies (model-splitting and reweighing) to resolve this drift, aiming to improve the overall conformance of models to the underlying data.
  arXiv  Detail & Related papers  (2023-03-30T17:30:42Z)
• DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks [71.6879432974126]
  We introduce DECAF: a GAN-based fair synthetic data generator for tabular data. We show that DECAF successfully removes undesired bias and is capable of generating high-quality synthetic data. We provide theoretical guarantees on the generator's convergence and the fairness of downstream models.
  arXiv  Detail & Related papers  (2021-10-25T12:39:56Z)
• Contrastive Model Inversion for Data-Free Knowledge Distillation [60.08025054715192]
  We propose Contrastive Model Inversion, where the data diversity is explicitly modeled as an optimizable objective. Our main observation is that, under the constraint of the same amount of data, higher data diversity usually indicates stronger instance discrimination. Experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet demonstrate that CMI achieves significantly superior performance when the generated data are used for knowledge distillation.
  arXiv  Detail & Related papers  (2021-05-18T15:13:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.