Copy Suppression

Copy suppression, broadly defined, refers to techniques that selectively reduce or eliminate unwanted information or signals within a system. Current research focuses on diverse applications, including enhancing speech quality by mitigating noise and reverberation (using methods like restorative speech enhancement and task-decoupling neural networks), improving the robustness of machine learning models by suppressing backdoor effects or unwanted adaptive stepsize ranges, and enhancing image and video quality by suppressing unwanted artifacts like glow or background features. These advancements have significant implications for various fields, from improving human-computer interaction and automated content moderation to enhancing the safety and reliability of autonomous systems and improving the accuracy of machine learning models.