Distortion Realism

Distortion realism research focuses on understanding and mitigating the effects of distortions in various data modalities, aiming to improve the robustness and accuracy of machine learning models and systems. Current research employs diverse approaches, including deep neural networks (like Vision Transformers and generative adversarial networks), probing methods to analyze model outputs, and novel loss functions to optimize for both realism and low distortion. This work is crucial for advancing numerous applications, from improving image quality assessment and anomaly detection to enhancing the robustness of scene text recognition and natural language processing models in the face of noisy or incomplete data.