Publications

We develop two plug-and-play velocity correction methods to improve inversion stability and editing fidelity in flow-based generative models. Achieved state-of-the-art results on PIE-Bench.

We develop an inversion-based fine-tuning method for dataset distillation and dataset generation, along with a cluster-based group sampling method for distilled data sampling.

We apply deep learning techniques to assist physicians in diagnosing coronary artery disease. We propose the Deformable Dilatable U-structure Module to specialize generic networks for coronary stenosis detection and improve their generalization ability. We also construct the 302 dataset for evaluation in real-world clinical scenarios.