UnsupervisedRepresentationLearningfrom Pre-trainedDiffusionProbabilisticModels

Drawing inspiration from this method that uses the prior knowledges (class label) to fill the gap, we aim to inversely extract the knowledges from the gap, i.e., learn representations that can help to fill the gap.