MIT researchers have developed StableRep, an AI training method using synthetic images generated by text-to-image models, which surpasses traditional training on real images. The approach leverages multi-positive contrastive learning, promising more efficient, less biased, and resource-conscious machine learning development.
Computers possess two remarkable capabilities with respect to images: They can both identify them and generate them anew. Historically, these functions have stood separate, akin to the disparate acts of a chef who is good at creating dishes (generati
A new framework known as MAGE merges the two key tasks of image generation and recognition, typically trained separately, into a single cohesive system. The work was developed by researchers from MIT and Google.