Rebalancing the Scales: A Systematic Mapping Study of Generative Adversarial Networks (GANs) in Addressing Data Imbalance

• URL: http://arxiv.org/abs/2502.16535v1
• Date: Sun, 23 Feb 2025 11:03:29 GMT
• Title: Rebalancing the Scales: A Systematic Mapping Study of Generative Adversarial Networks (GANs) in Addressing Data Imbalance
• Authors: Pankaj Yadav, Gulshan Sihag, Vivek Vijay,
• Abstract summary: Generative Adrial Networks (GANs) showed immense potential as a data preprocessing technique that generates good quality synthetic data.<n>This study employs a systematic mapping methodology to analyze 3041 papers on GAN-based sampling techniques for imbalanced data sourced from four digital libraries.<n>Through comprehensive quantitative analysis, this research introduces three categorization mappings as application domains, GAN techniques, and GAN variants used to handle the imbalanced nature of the data.
• Score: 0.16385815610837165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning algorithms are used in diverse domains, many of which face significant challenges due to data imbalance. Studies have explored various approaches to address the issue, like data preprocessing, cost-sensitive learning, and ensemble methods. Generative Adversarial Networks (GANs) showed immense potential as a data preprocessing technique that generates good quality synthetic data. This study employs a systematic mapping methodology to analyze 3041 papers on GAN-based sampling techniques for imbalanced data sourced from four digital libraries. A filtering process identified 100 key studies spanning domains such as healthcare, finance, and cybersecurity. Through comprehensive quantitative analysis, this research introduces three categorization mappings as application domains, GAN techniques, and GAN variants used to handle the imbalanced nature of the data. GAN-based over-sampling emerges as an effective preprocessing method. Advanced architectures and tailored frameworks helped GANs to improve further in the case of data imbalance. GAN variants like vanilla GAN, CTGAN, and CGAN show great adaptability in structured imbalanced data cases. Interest in GANs for imbalanced data has grown tremendously, touching a peak in recent years, with journals and conferences playing crucial roles in transmitting foundational theories and practical applications. While with these advances, none of the reviewed studies explicitly explore hybridized GAN frameworks with diffusion models or reinforcement learning techniques. This gap leads to a future research idea develop innovative approaches for effectively handling data imbalance.

Related papers

• Comparing Methods for Bias Mitigation in Graph Neural Networks [5.256237513030105]
  This paper examines the critical role of Graph Neural Networks (GNNs) in data preparation for generative artificial intelligence (GenAI) systems. We present a comparative analysis of three distinct methods for bias mitigation: data sparsification, feature modification, and synthetic data augmentation.
  arXiv  Detail & Related papers  (2025-03-28T16:18:48Z)
• Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain Study [49.5374512525016]
  Medical image datasets are essential for training models used in computer-aided diagnosis, treatment planning, and medical research. Some challenges are associated with these datasets, including variability in data distribution, data scarcity, and transfer learning issues when using models pre-trained from generic images. We propose a methodology based on Siamese neural networks in which a series of techniques are integrated to mitigate the effects of data scarcity and distribution imbalance.
  arXiv  Detail & Related papers  (2024-01-18T16:59:27Z)
• Towards Understanding How Data Augmentation Works with Imbalanced Data [17.478900028887537]
  We study the effect of data augmentation on three different classifiers, convolutional neural networks, support vector machines, and logistic regression models. Our research indicates that DA, when applied to imbalanced data, produces substantial changes in model weights, support vectors and feature selection. We hypothesize that DA works by facilitating variances in data, so that machine learning models can associate changes in the data with labels.
  arXiv  Detail & Related papers  (2023-04-12T15:01:22Z)
• Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach [83.2160310392168]
  This paper proposes FS-GAN to support automatic traffic analysis and synthesis over a large number of heterogeneous datasets. FS-GAN is composed of multiple distributed Generative Adversarial Networks (GANs) FS-GAN can classify data of unknown types of service and create synthetic samples that capture the traffic distribution of the unknown types.
  arXiv  Detail & Related papers  (2023-02-01T03:23:11Z)
• FairGen: Fair Synthetic Data Generation [0.3149883354098941]
  We propose a pipeline to generate fairer synthetic data independent of the GAN architecture. We claim that while generating synthetic data most GANs amplify bias present in the training data but by removing these bias inducing samples, GANs essentially focuses more on real informative samples.
  arXiv  Detail & Related papers  (2022-10-24T08:13:47Z)
• An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentation [91.62129090006745]
  This paper studies the distribution shift problem from the perspective of pre-training and data augmentation. We provide the first comprehensive empirical study focusing on pre-training and data augmentation.
  arXiv  Detail & Related papers  (2022-05-25T13:04:53Z)
• DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup [58.894901088797376]
  federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data. We propose a general framework to solve the above two challenges simultaneously. We provide comprehensive theoretical analysis including robustness analysis, convergence analysis, and generalization ability.
  arXiv  Detail & Related papers  (2022-04-16T08:08:29Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• On the Fairness of Generative Adversarial Networks (GANs) [1.061960673667643]
  Generative adversarial networks (GANs) are one of the greatest advances in AI in recent years. In this paper, we analyze and highlight fairness concerns of GANs model.
  arXiv  Detail & Related papers  (2021-03-01T12:25:01Z)
• Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data [77.88594632644347]
  Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization challenge. We propose a novel momentum-based method to mitigate this decentralized training difficulty.
  arXiv  Detail & Related papers  (2021-02-09T11:27:14Z)
• Mitigating Dataset Imbalance via Joint Generation and Classification [17.57577266707809]
  Supervised deep learning methods are enjoying enormous success in many practical applications of computer vision. The marked performance degradation to biases and imbalanced data questions the reliability of these methods. We introduce a joint dataset repairment strategy by combining a neural network classifier with Generative Adversarial Networks (GAN) We show that the combined training helps to improve the robustness of both the classifier and the GAN against severe class imbalance.
  arXiv  Detail & Related papers  (2020-08-12T18:40:38Z)

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.