Semantic Corruption

Semantic corruption research investigates how artificial intelligence models, particularly deep learning systems, respond to alterations in input data that affect meaning or context, rather than simply visual or textual fidelity. Current research focuses on developing robust models across various modalities (image, text, 3D point clouds) using techniques like dynamic sparse training, contrastive learning, and test-time adaptation, often leveraging diffusion models for generating realistic corruptions. This work is crucial for improving the reliability and generalizability of AI systems in real-world applications where noisy or incomplete data is common, ultimately leading to more trustworthy and dependable AI.