Pseudo Sample

Pseudo sample generation focuses on creating synthetic data that mimics real-world data characteristics, primarily to address data scarcity or noise in machine learning and other fields. Current research emphasizes using diffusion models, generative adversarial networks, and knowledge distillation techniques to synthesize high-fidelity samples, often incorporating strategies like sample selection and debiasing to improve quality and robustness. This work is significant because it enables training of machine learning models in situations with limited or noisy data, improving model performance and expanding the applicability of data-driven methods across various scientific domains and practical applications.