Semantic Noise

Semantic noise, encompassing inaccuracies and irrelevant information in data representations, hinders the performance of various machine learning tasks. Current research focuses on developing robust models and algorithms, such as contrastive learning and adversarial training, to mitigate the effects of this noise across diverse applications including knowledge graphs, image quality assessment, and named entity recognition. These efforts leverage techniques like feature selection, quantization-based decomposition, and masked autoencoders to improve the accuracy and efficiency of systems susceptible to semantic noise. The resulting advancements have significant implications for improving the reliability and performance of numerous AI systems.