Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?

Code

• github.com/google-deepmind/relicv2
  Officialjax★ 5

Abstract

Despite recent progress made by self-supervised methods in representation learning with residual networks, they still underperform supervised learning on the ImageNet classification benchmark, limiting their applicability in performance-critical settings. Building on prior theoretical insights from ReLIC [Mitrovic et al., 2021], we include additional inductive biases into self-supervised learning. We propose a new self-supervised representation learning method, ReLICv2, which combines an explicit invariance loss with a contrastive objective over a varied set of appropriately constructed data views to avoid learning spurious correlations and obtain more informative representations. ReLICv2 achieves 77.1\% top-1 accuracy on ImageNet under linear evaluation on a ResNet50, thus improving the previous state-of-the-art by absolute +1.5\%; on larger ResNet models, ReLICv2 achieves up to 80.6\% outperforming previous self-supervised approaches with margins up to +2.3\%. Most notably, ReLICv2 is the first unsupervised representation learning method to consistently outperform the supervised baseline in a like-for-like comparison over a range of ResNet architectures. Using ReLICv2, we also learn more robust and transferable representations that generalize better out-of-distribution than previous work, both on image classification and semantic segmentation. Finally, we show that despite using ResNet encoders, ReLICv2 is comparable to state-of-the-art self-supervised vision transformers.

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