CVPR 2023 - Train-Once-for-All Personalization

In this episode we discuss Train-Once-for-All Personalization by Authors: - Hong-You Chen - Yandong Li - Yin Cui - Mingda Zhang - Wei-Lun Chao - Li Zhang Affiliations: - Hong-You Chen and Wei-Lun Chao are affiliated with The Ohio State University. - Yandong Li, Yin Cui, Mingda Zhang, and Li Zhang are affiliated with Google Research. Contact information: - Hong-You Chen and Wei-Lun Chao: Yandong Li, Yin Cui, Mingda Zhang, and Li Zhang:  The paper proposes a framework called Train-once-for-All PERsonalization (TAPER) for training a "personalization-friendly" model that can be customized for different end-users based on their task descriptions. The framework learns a set of "basis" models and a mixer predictor, which can combine the weights of the basis models on-the-fly to create a personalized model for a given end-user. TAPER consistently outperforms baseline methods and can synthesize smaller models for deployment on resource-limited devices, and can even be specialized without task descriptions based on past predictions.