Generative Data Augmentation Framework

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A Generative Data Augmentation Framework is a data augmentation framework that is a generative AI framework and can be used to create a synthetic data augmentation system to solve synthetic data augmentation tasks.



References

2023

  • Claude2
    • A generative data augmentation framework is a system that automatically generates synthetic data to augment the training data for machine learning models. The key aspects are:
      • [[Generative: The synthetic data is generated by the system rather than just making modifications to the existing training data. This allows creating entirely new data points.
      • [[Data Augmentation: The goal is to increase the size and diversity of the training data to improve model performance, especially in low-resource scenarios with limited data.
      • [[Framework: It consists of components like generation models, corruption strategies, training schemes etc. that work together to enable controlled generation of augmented data.

2023

2021

  • (Naaz et al., 2021) ⇒ F Naaz, A Herle, J Channegowda, A Raj. (2021). “A generative adversarial network‐based synthetic data augmentation technique for battery condition evaluation." In: International Journal of Energy. [DOI Not Provided]
    • NOTE: It discusses the use of a generative adversarial network-based synthetic data augmentation framework for evaluating battery conditions.

2021

  • (Sajjad et al., 2021) ⇒ M Sajjad, F Ramzan, MUG Khan. (2021). “Deep convolutional generative adversarial network for Alzheimer's disease classification using positron emission tomography (PET) and synthetic data augmentation." In: Microscopy Research and Technique. [DOI Not Provided]
    • NOTE: It describes the application of deep convolutional generative adversarial networks for Alzheimer's disease classification, using synthetic data augmentation techniques on Positron Emission Tomography (PET).

2020

  • (Li et al., 2020) ⇒ X Li, J Luo, R Younes. (2020). “ActivityGAN: Generative adversarial networks for data augmentation in sensor-based human activity recognition." In: Adjunct Proceedings of the 2020 ACM Conference.
    • NOTE: It explains the concept of ActivityGAN, a synthetic data augmentation framework, used for sensor-based human activity recognition.