RevColV2: Exploring Disentangled Representations in Masked Image Modeling Qi Han

In this paper, we propose a new architecture, RevColV2, which tackles this issue by keeping the entire autoen-coder architecture during both pre-training and fine-tuning. The main body of RevColV2 contains bottom-up columns and top-down columns, between which information is reversibly propagated and gradually disentangled.