Synthetic Generation

Synthetic generation focuses on creating artificial data that mimics real-world data characteristics, primarily to address data scarcity, bias, or privacy concerns in various fields. Current research emphasizes the use of generative adversarial networks (GANs), diffusion models, and imitation learning algorithms to produce realistic synthetic data, often tailored to specific applications like medical image analysis, code generation, and toxicity detection. This approach significantly impacts scientific research by enabling the training and evaluation of machine learning models in data-limited scenarios and improving the robustness and fairness of these models in practical applications.