Synthetic Data Augmentation Task

A Synthetic Data Augmentation Task is a data augmentation task that uses synthetic data.

• Context:
  • It can (typically) generate synthetic data to augment the initial dataset.
  • It can (often) be utilized to improve the performance of machine learning models, especially in scenarios where existing data is limited.
  • It can be supported by a Synthestic Data Augmentation System, that may
    • involve various techniques such as bootstrapping, up-sampling, and down-sampling.
    • make use of Generative Adversarial Networks (GANs) or other generative models to create synthetic data.
    • ...
• Example(s):
  • Modifying real-world images, such as through rotation and translation, to improve the performance in image classification model training.
  • Generating synthetic images to improve the performance in image classification model training.
  • Artificial expansion of a legal NLP dataset using synthetically generated legal language.
  • ...
• Counter-Example(s):
  • Simply duplicating existing samples in the dataset.
  • Altering the class labels without modifying the feature values.
• See: Synthetic Data Augmentation System, Synthetic Data Augmentation Framework, Generative Data Augmentation Framework, Data Augmentation.

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References

GBard

• GBard
  • Synthetic data augmentation is the process of generating artificial data that is similar to real-world data, for the purpose of training machine learning models. This can be done in a variety of ways, depending on the type of data being augmented and the desired outcome.
  • Synthetic data augmentation can be used to improve the performance of machine learning models in a number of ways. First, it can increase the size and diversity of the training set. This can help to prevent the model from overfitting to the training data. Second, synthetic data can be used to generate data that is difficult or expensive to collect in the real world. For example, synthetic data could be used to generate data on rare events or events that are dangerous or disruptive to collect real-world data for.
  • Here are some examples of synthetic data augmentation tasks:
    • Generating new images of faces for facial recognition models. This could be done by using a GAN to generate new faces that look like real faces, or by using a simulation of a human face to generate images of faces under different conditions (e.g., different lighting, different angles, etc.).
    • Generating new text data for natural language processing (NLP) models. This could be done by using a GAN to generate new sentences that look like real sentences, or by using a simulation of a conversation to generate transcripts of conversations.
    • Generating new sensor data for self-driving car models. This could be done by using a simulation of a self-driving car to generate data on how the car would behave in different situations (e.g., different road conditions, different traffic conditions, etc.).

2023

• (Ghosh et al., 2023) ⇒ Sreyan Ghosh, Chandra Kiran Evuru, Sonal Kumar, S. Ramaneswaran, S. Sakshi, Utkarsh Tyagi, and Dinesh Manocha. (2023). “DALE: Generative Data Augmentation for Low-Resource Legal NLP.” In: arXiv preprint arXiv:2310.15799.
  • NOTE: It details how the DALE Framework, a generative data augmentation framework, was used to increase the amount and diversity of data for low-resource legal natural language processing (NLP) tasks.

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.
  • QUOTE: “Our study aimed to develop an GANs-based synthetic data augmentation technique to evaluate battery conditions.”
  • NOTE: It describes an application of synthetic data augmentation in the domain of battery condition evaluation.

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.
  • QUOTE: “We present a deep convolutional generative adversarial network for Alzheimer's disease classification by augmenting the input data using synthetic positron emission tomography (PET) scans.”
  • NOTE: It provides an example of synthetic data augmentation applied to medical imaging, specifically for Alzheimer's disease classification.

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