However, the labeling process can be tedious, costly, and error-prone in practice. In this paper, we propose to unify active learning (AL) and message passing towards minimizing labeling costs, e.g.,making useoffewandunreliable labels thatcan beobtainedcheaply.

Then the phenomenon has been thought as inevitable. However, the phenomenon seldom occurs in the context of recent deep learning. There is a gap between theory and reality.

Vision-language pre-training methods, e.g., CLIP, demonstrate an impressive zero-shot performance on visual categorizations with the class proxy from the text embedding of the class name.

This yields a class ofenergy-inspired models(EIMs) that incorporate learned energyfunctions while stillproviding exactsamples andtractable log-likelihood lower bounds. We describe and evaluate three instantiations of such models based ontruncated rejection sampling, self-normalized importance sampling, and Hamiltonian importance sampling.

The pretraining-finetuning paradigm has gained widespread adoption in vision tasks and other fields. However, the finetuning phase still requires high-quality annotated samples.