The Impact of Generative Data Diversity on AI Accuracy

DiverGen demonstrates that variety, not volume, is the real factor influencing performance, redefining the use of generative data in computer vision. Using a variety of categories, prompts, and generative models, DiverGen improves instance segmentation through methodical tests on the LVIS dataset. Its Generative Data Diversity Enhancement (GDDE) methodology uses just generative data and produces better outcomes than cutting-edge techniques like X-Paste. Each design decision is validated by additional ablation investigations, which demonstrate that the SAM-bg annotation technique, quick diversity, and model mixing all work together to maximize quality and accuracy.