RareGAN: Generating Samples for Rare Classes

Lin, Zinan; Hao, Linlin; Fanti, Giulia; Sekar, Vyas

doi:10.1609/aaai.v36i7.20715

Cited by 6 publications

(5 citation statements)

References 44 publications

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“…Liu et al [198] studied how well GAN can approximate the target distribution under various notions of distributional convergence. Lin et al [199] showed that GAN-generated samples inherently satisfy some (weak) privacy guarantees under certain conditions. Another study offers a theoretical perspective on why GANs sometimes fail for certain generation tasks, in particular, sequential tasks such as natural language generation [200].…”

Section: Recent Theoretical Advancements Of Ganmentioning

confidence: 99%

Ten years of generative adversarial nets (GANs): a survey of the state-of-the-art

Chakraborty,

Reddy K S,

Naik

et al. 2024

Mach. Learn.: Sci. Technol.

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Generative Adversarial Networks (GANs) have rapidly emerged as powerful tools for generating realistic and diverse data across various domains, including computer vision and other applied areas, since their inception in 2014. Consisting of a discriminative network and a generative network engaged in a Minimax game, GANs have revolutionized the field of generative modeling. In February 2018, GAN secured the leading spot on the ``Top Ten Global Breakthrough Technologies List'' issued by the Massachusetts Science and Technology Review. Over the years, numerous advancements have been proposed, leading to a rich array of GAN variants, such as conditional GAN, Wasserstein GAN, CycleGAN, and StyleGAN, among many others. This survey aims to provide a general overview of GANs, summarizing the latent architecture, validation metrics, and application areas of the most widely recognized variants. We also delve into recent theoretical developments, exploring the profound connection between the adversarial principle underlying GAN and Jensen-Shannon divergence while discussing the optimality characteristics of the GAN framework. The efficiency of GAN variants and their model architectures will be evaluated along with training obstacles as well as training solutions. In addition, a detailed discussion will be provided, examining the integration of GANs with newly developed deep learning frameworks such as Transformers, Physics-Informed Neural Networks, Large Language models, and Diffusion models. Finally, we reveal several issues as well as future research outlines in this field.

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Section: Recent Theoretical Advancements Of Ganmentioning

confidence: 99%

Ten years of generative adversarial nets (GANs): a survey of the state-of-the-art

Chakraborty,

Reddy K S,

Naik

et al. 2024

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Thus, both parties improve their respective skills over time. As the generator does not have access to the training data and, thus, creates entirely new data points, this model architecture implies privacy [8].…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

“…Hence, it is often challenging to build appropriate machine learning algorithms as data availability remains an issue [7]. The creation of synthetic data through generative adversarial networks (GAN) could be one solution to tackle this field of problems connected to EHR as the creation of synthetic data through the application of GANs not only implicates privacy [8] but is also able to remedy data scarcity or an imbalance in data sets [9]. Introduced in 2014 by Goodfellow et al [10], the research related to GANs has surged since then.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Data as a Proxy for Real-World Electronic Health Records in the Patient Length of Stay Prediction

Bietsch,

Stahlbock,

Voß

2023

Sustainability

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While generative artificial intelligence has gained popularity, e.g., for the creation of images, it can also be used for the creation of synthetic tabular data. This bears great potential, especially for the healthcare industry, where data are often scarce and underlie privacy restrictions. For instance, the creation of synthetic electronic health records (EHR) promises to improve the usage of machine learning algorithms, which usually work with large amounts of data. This also applies for the prediction of the patient length of stay (LOS), a key measure for hospitals. Thereby, the LOS represents one of the core tools for decision makers to plan the allocation of resources. Thus, this paper aims to add to the still-young research concerning the application of generative adversarial nets (GAN) on tabular EHR. It does that with the intention to leverage the advantages of synthetic data for the prediction of the LOS in order to contribute to the efficiency-enhancing and cost-saving aspirations of hospitals and insurance companies. Therefore, the applicability of synthetic data that is generated using GANs as a proxy for scarce real-world EHR for the patient LOS multi-class classification task is examined. In this context, the Conditional Tabular GAN (CTGAN) and the Copula GAN are selected as the underlying models as they are state-of-the-art GAN architectures designed for generating synthetic tabular data. The CTGAN is found to be the superior model for the underlying use case. Nevertheless, the paper shows that there is still room for improvement when applying state-of-the-art GAN architectures to clinical healthcare data.

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“…In , a supervised contrastive learning method is designed that adds a loss term between the samples and the pre-defined class centers to regularize representation learning with geometric priors. CMO (Park et al 2022) and RareGAN (Lin et al 2022) are the latest data-balancing methods for deep imbalance learning. But their settings are different from ours, since we deal with labeled data and aim to improve the performance of imbalance learning through self-training.…”

Section: Related Workmentioning

confidence: 99%

Quality-Aware Self-Training on Differentiable Synthesis of Rare Relational Data

Zhang

Hou

Chen

et al. 2023

AAAI

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Data scarcity is a very common real-world problem that poses a major challenge to data-driven analytics. Although a lot of data-balancing approaches have been proposed to mitigate this problem, they may drop some useful information or fall into the overfitting problem. Generative Adversarial Network (GAN) based data synthesis methods can alleviate such a problem but lack of quality control over the generated samples. Moreover, the latent associations between the attribute set and the class labels in a relational data cannot be easily captured by a vanilla GAN. In light of this, we introduce an end-to-end self-training scheme (namely, Quality-Aware Self-Training) for rare relational data synthesis, which generates labeled synthetic data via pseudo labeling on GAN-based synthesis. We design a semantic pseudo labeling module to first control the quality of the generated features/samples, then calibrate their semantic labels via a classifier committee consisting of multiple pre-trained shallow classifiers. The high-confident generated samples with calibrated pseudo labels are then fed into a semantic classification network as augmented samples for self-training. We conduct extensive experiments on 20 benchmark datasets of different domains, including 14 industrial datasets. The results show that our method significantly outperforms state-of-the-art methods, including two recent GAN-based data synthesis schemes. Codes are available at https://github.com/yaxinhou/QAST.

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RareGAN: Generating Samples for Rare Classes

Cited by 6 publications

References 44 publications

Ten years of generative adversarial nets (GANs): a survey of the state-of-the-art

Ten years of generative adversarial nets (GANs): a survey of the state-of-the-art

Synthetic Data as a Proxy for Real-World Electronic Health Records in the Patient Length of Stay Prediction

Quality-Aware Self-Training on Differentiable Synthesis of Rare Relational Data

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