Synthetic Imagery Sets New Bar in AI Training Efficiency

This work introduces StableRep, a method that leverages synthetic images generated by text-to-image models like Stable Diffusion for AI training. The study demonstrates that models trained exclusively with synthetic images outperform those trained with real images in large-scale settings, using multi-positive contrastive learning. StableRep+ (an enhanced variant with language supervision) achieves superior accuracy and efficiency compared to CLIP models trained on 50 million real images, using only 20 million synthetic images. The paper notes challenges like slow image generation and potential biases but underscores the potential for synthetic data to reduce reliance on real data collection. To read the full article go here... https://openreview.net/forum?id=xpjsOQtKqx