Data Consistency

Data consistency in image processing and machine learning focuses on ensuring that generated or reconstructed data accurately reflects the underlying measurements or observations, addressing discrepancies that arise from various sources like sparse data or conflicting training sets. Current research emphasizes integrating data consistency mechanisms within powerful generative models, such as diffusion models, often employing techniques like alternating optimization or decoupled processing to improve efficiency and accuracy. This work is crucial for advancing applications in diverse fields, including medical imaging (e.g., CT, SPECT reconstruction) and multimodal large language models, where reliable and accurate data reconstruction is paramount for improved diagnostic capabilities and model performance.