Social Reward: Evaluating and Enhancing Generative AI through Million-User Feedback from an Online Creative Community
Authors: Arman Isajanyan,Artur Shatveryan,David Kocharian,Zhangyang Wang,Humphrey Shi
Presentation: ICLR 2024,Spotlight

Abstract: Social reward as a form of community recognition provides a strong source of
motivation for users of online platforms to actively engage and contribute with
content to accumulate peers approval. In the realm of text-conditioned image
synthesis, the recent surge in progress has ushered in a collaborative era where
users and AI systems coalesce to refine visual creations. This co-creative pro-
cess in the landscape of online social networks empowers users to craft original
visual artworks seeking for community validation. Nevertheless, assessing these
models in the context of collective community preference introduces distinct chal-
lenges. Existing evaluation methods predominantly center on limited size user
studies guided by image quality and alignment with prompts. This work pio-
neers a paradigm shift, unveiling Social Reward - an innovative reward modeling
framework that leverages implicit feedback from social network users engaged
in creative editing of generated images. We embark on an extensive journey of
dataset curation and refinement, drawing from Picsart: an online visual creation
and editing platform, yielding a first million-user-scale dataset of implicit human
preferences for user-generated visual art named Picsart Image-Social. Our anal-
ysis exposes the shortcomings of current metrics in modeling community creative
preference of text-to-image models’ outputs, compelling us to introduce a novel
predictive model explicitly tailored to address these limitations. Rigorous quan-
titative experiments and user study show that our Social Reward model aligns
better with social popularity than existing metrics. Furthermore, we utilize So-
cial Reward to fine-tune text-to-image models, yielding images that are more fa-
vored by not only Social Reward, but also other established metrics. These find-
ings highlight the relevance and effectiveness of Social Reward in assessing com-
munity appreciation for AI-generated artworks, establishing a closer alignment
with users’ creative goals: creating popular visual art. Codes can be accessed at
https://github.com/Picsart-AI-Research/Social-Reward