DiverGen Makes Large-Scale Instance Segmentation Training More Effective

DiverGen presents a diversity-driven, scalable method for generative data augmentation, such as segmentation. DiverGen examines the influence of generating samples via the perspective of distribution discrepancy, in contrast to previous works that approach them as straightforward supplements for sparse data. This demonstrates how varied synthetic data reduces overfitting and broadens the learnable data space.