  Representation learning has been evolving from traditional supervised
training to Contrastive Learning (CL) and Masked Image Modeling (MIM). Previous
works have demonstrated their pros and cons in specific scenarios, i.e., CL and
supervised pre-training excel at capturing longer-range global patterns and
enabling better feature discrimination, while MIM can introduce more local and
diverse attention across all transformer layers. In this paper, we explore how
to obtain a model that combines their strengths. We start by examining previous
feature distillation and mask feature reconstruction methods and identify their
limitations. We find that their increasing diversity mainly derives from the
asymmetric designs, but these designs may in turn compromise the discrimination
ability. In order to better obtain both discrimination and diversity, we
propose a simple but effective Hybrid Distillation strategy, which utilizes
both the supervised/CL teacher and the MIM teacher to jointly guide the student
model. Hybrid Distill imitates the token relations of the MIM teacher to
alleviate attention collapse, as well as distills the feature maps of the
supervised/CL teacher to enable discrimination. Furthermore, a progressive
redundant token masking strategy is also utilized to reduce the distilling
costs and avoid falling into local optima. Experiment results prove that Hybrid
Distill can achieve superior performance on different benchmarks.
