Clean Data

Clean data, crucial for reliable machine learning, is often unavailable or compromised in various applications. Current research focuses on developing methods to purify noisy or poisoned datasets, often employing generative adversarial networks (GANs), diffusion models, and energy-based models to either remove noise or identify and correct corrupted data points without relying on separate clean datasets. These techniques are vital for improving the robustness and reliability of machine learning models across diverse fields, including bioacoustics, medical imaging, and cybersecurity, where access to perfectly clean data is often impractical or impossible. The development of effective data purification methods is essential for advancing the trustworthiness and real-world applicability of machine learning.