Data Diversity Matters More Than Data Quantity in AI
No se pudo agregar al carrito
Add to Cart failed.
Error al Agregar a Lista de Deseos.
Error al eliminar de la lista de deseos.
Error al añadir a tu biblioteca
Error al seguir el podcast
Error al dejar de seguir el podcast
-
Narrado por:
-
De:
This story was originally published on HackerNoon at: https://hackernoon.com/data-diversity-matters-more-than-data-quantity-in-ai.
DiverGen demonstrates that superior instance segmentation performance is driven by data diversity rather than quantity.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #diffusion-models, #instance-segmentation, #data-diversity, #long-tail-recognition, #data-scaling, #x-paste-comparison, #model-performance-analysis, #generative-data-augmentation, and more.
This story was written by: @instancing. Learn more about this writer by checking @instancing's about page, and for more stories, please visit hackernoon.com.
In order to verify the effect of generating data variety in instance segmentation, this part tests DiverGen on the LVIS dataset. Experiments show that improving data diversity—through category, prompt, and model variation—drives sustained accuracy improvements, but increasing data quantity alone eventually plateaus or lowers performance.