Hybrid Noise

Hybrid noise, encompassing both feature and label inaccuracies in data, presents a significant challenge in machine learning and signal processing. Current research focuses on developing robust learning frameworks and algorithms, often employing techniques like low-rank approximation and alternating direction method of multipliers (ADMM) to recover clean data from noisy inputs, as well as diffusion models for realistic noise synthesis. These advancements aim to improve the reliability and performance of models trained on real-world, imperfect data, impacting fields ranging from image and audio processing to robotics and telepresence systems.