Input Corruption

Input corruption, the degradation of data entering a machine learning model, significantly impacts performance across various applications, from robotic vision to medical image analysis and speech recognition. Current research focuses on developing robust models through techniques like test-time adaptation using diffusion models, per-corruption normalization adjustments, and multimodal fusion strategies that leverage reliable information streams even when some inputs are corrupted. These advancements aim to improve the reliability and accuracy of AI systems in real-world scenarios where imperfect or noisy data is unavoidable, impacting fields ranging from autonomous driving to healthcare diagnostics.