​​StableRep: Synthetic Images from Text-to-Image Models Make S | Data Science by ODS.ai 🦜

​​StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners

In a ground-breaking exploration of visual representation learning, researchers have leveraged synthetic images produced by leading text-to-image models, specifically Stable Diffusion, achieving promising results. The study uncovers two key insights - firstly, when configured correctly, self-supervised methods trained on synthetic images can match or even outperform those trained on real images. This suggests an exciting avenue for efficient and effective representation learning, reducing the need for extensive real image datasets.

Secondly, the researchers have devised a novel approach called StableRep, a multi-positive contrastive learning method that treats multiple images, generated from the same text prompt, as mutual positives. The compelling finding is that StableRep, trained solely with synthetic images, outperforms representations learned by prominent methods such as SimCLR and CLIP, even when these used real images. In a striking demonstration, when language supervision is added, StableRep trained with 20M synthetic images outperforms CLIP trained with a whopping 50M real images. These findings not only underscore the potential of synthetic data but also pave the way for more efficient, large-scale visual representation learning.

Paper link: https://arxiv.org/abs/2306.00984

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-stablerep

#deeplearning #cv #nlp #stablediffusion #texttoimage #syntheticdata