P Norm Corruption

P-norm corruption research investigates the robustness of machine learning models to various types of data corruption, focusing on how well models can handle noise and distortions introduced into input data, such as those found in OCR errors or adversarial attacks. Current research explores algorithms and model architectures designed to mitigate the effects of these corruptions, including methods leveraging synthetic data for training and novel loss functions that incorporate the corruption process directly into model learning. This work is significant for improving the reliability and generalizability of machine learning models across diverse real-world applications, where data imperfections are common, and for furthering our understanding of model vulnerabilities and strengths.