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

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 performancecritical settings. Building on prior theoretical insights (Mitrovic et al., 2021) we propose RELICv2 which combines an explicit invariance loss with a contrastive objective over a varied set of appropriately constructed data views. RELICv2 achieves 77.1% top-1 classification accuracy on ImageNet using linear evaluation with a ResNet50 architecture and 80.6% with larger ResNet models, outperforming previous state-ofthe-art self-supervised approaches by a wide margin. Most notably, RELICv2 is the first representation learning method to consistently outperform the supervised baseline in a like-for-like comparison using a range of standard ResNet architectures. Finally we show that despite using ResNet encoders, RELICv2 is comparable to state-of-theart self-supervised vision transformers.