Title: Mugs: A Multi-Granular Self-Supervised  Learning Framework. Abstract: In self-supervised learning,  multi-granular features  are  heavily desired though  rarely investigated, as different downstream tasks (e.g., general and fine-grained classification)  often require different or multi-granular features, e.g.~fine- or coarse-grained one or  their mixture.  In this work, for the first time, we propose an effective MUlti-Granular Self-supervised  learning (Mugs) framework to explicitly learn  multi-granular visual features. Mugs has three complementary granular supervisions:  1) an instance discrimination supervision (IDS),  2) a novel  local-group discrimination supervision (LGDS),   and 3) a group discrimination supervision (GDS).   IDS distinguishes different instances to learn  instance-level fine-grained features. LGDS aggregates features of an image and its neighbors into a local-group feature, and pulls local-group features from different crops of the same image together and push them away for others.   It provides complementary instance supervision to IDS via an extra alignment on local neighbors, and scatters different local-groups separately to increase discriminability.   Accordingly, it helps learn high-level fine-grained features at a local-group level. Finally, to prevent similar local-groups from being scattered randomly or far away,  GDS brings similar samples close and thus pulls similar local-groups together,  capturing coarse-grained features at a (semantic) group level. Consequently,  Mugs can  capture   three  granular features that  often enjoy higher generality  on diverse downstream tasks over single-granular features, e.g.~instance-level fine-grained features in contrastive learning.  By only pretraining on ImageNet-1K,  Mugs sets new SoTA linear probing accuracy 81.9$\%$ on ImageNet-1K and improves previous  SoTA by  $0.9\%$.  	It also surpasses SoTAs on  other tasks,  e.g.~transfer learning,  detection  and   segmentation.