2 papers
cs.CL2024
Calibrate to Discriminate: Improve In-Context Learning with Label-Free Comparative Inference
Wei Cheng, Tianlu Wang, Yanmin Ji +3
While in-context learning with large language models (LLMs) has shown impressive performance, we have discovered a unique miscalibration behavior where both correct and incorrect p…
cs.CV2023
ProS: Facial Omni-Representation Learning via Prototype-based Self-Distillation
Xing Di, Yiyu Zheng, Xiaoming Liu +1
This paper presents a novel approach, called Prototype-based Self-Distillation (ProS), for unsupervised face representation learning. The existing supervised methods heavily rely o…