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MIM-Refiner refines the representation of pre-trained Masked Image Models (MIM) by attaching Instance Discrimination (ID) heads to multiple intermediate heads. This setup is then trained for a few epochs with with our Nearest Neighbor Alignment (NNA) objective.

MIM-Refiner drastically advances state-of-the-art in ImageNet-1K linear probing. It achieves an improvement of +2.5% over the previous state-of-the-art. In comparison, over the last 4 years, state-of-the-art improved by +2.6%.