DiverGen Proves AI Models Learn Better with Variety
Description
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DiverGen uses accurate SAM-based annotation methods, generative models, and a variety of prompts to improve AI segmentation.
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This section describes DiverGen's comprehensive implementation and visualization techniques. To verify generative diversity, the authors use UMAP visualization and CLIP-based data distribution analysis. While ChatGPT-generated prompts increase textual variety and visual richness, they also improve generative model diversity through the use of Stable Diffusion and DeepFloyd-IF. Compared to previous methods like max CLIP or SAM-foreground, the suggested SAM-background (SAM-bg) annotation method generates more precise and comprehensive masks.
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