Comprehensive Exploration of Synthetic Data Generation: A Survey
CoRR(2024)
摘要
Recent years have witnessed a surge in the popularity of Machine Learning
(ML), applied across diverse domains. However, progress is impeded by the
scarcity of training data due to expensive acquisition and privacy legislation.
Synthetic data emerges as a solution, but the abundance of released models and
limited overview literature pose challenges for decision-making. This work
surveys 417 Synthetic Data Generation (SDG) models over the last decade,
providing a comprehensive overview of model types, functionality, and
improvements. Common attributes are identified, leading to a classification and
trend analysis. The findings reveal increased model performance and complexity,
with neural network-based approaches prevailing, except for privacy-preserving
data generation. Computer vision dominates, with GANs as primary generative
models, while diffusion models, transformers, and RNNs compete. Implications
from our performance evaluation highlight the scarcity of common metrics and
datasets, making comparisons challenging. Additionally, the neglect of training
and computational costs in literature necessitates attention in future
research. This work serves as a guide for SDG model selection and identifies
crucial areas for future exploration.
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