MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Masked Image Modeling Representations

Benedikt Alkin, Lukas Miklautz, Sepp Hochreiter, Johannes Brandstetter

Keywords: self-supervised learning, masked image modeling, instance discrimination, computer vision, contrastive learning

TL;DR: We introduce MIM-Refiner, a refinement process of MIM(masked image modeling)-models using an ensemble of instance discrimination heads attached at intermediate layers to leverage the best pre-trained MIM representations.

Abstract:

We introduce MIM (Masked Image Modeling)-Refiner, a contrastive learning boost for pre-trained MIM models. MIM-Refiner is motivated by the insight that strong representations within MIM models generally reside in intermediate layers. Accordingly, MIM-Refiner leverages multiple instance discrimination (ID) heads that are connected to different intermediate layers. In each head, a nearest neighbor ID objective constructs clusters that capture semantic information which improves performance on downstream tasks, including off-the-shelf and fine-tuning settings.

The refinement process is short and simple - yet highly effective. Within a few epochs, we refine the features of MIM models from subpar to state-of-the-art, off-the-shelf features. Refining a ViT-H, pre-trained with data2vec 2.0 on ImageNet-1K, sets a new state-of-the-art in linear probing (84.7\%) and low-shot classification among models that are pre-trained on ImageNet-1K. MIM-Refiner efficiently combines the advantages of MIM and ID objectives, enabling scaling ID objectives to billion parameter models using relatively little compute. MIM-Refiner compares favorably against previous state-of-the-art SSL models on various benchmarks such as low-shot classification, long-tailed classification and semantic segmentation.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 2950