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Representation learning promises to unlock deep learning for the long tail ofvision tasks without expensive labelled datasets. Yet, the absence of a unifiedevaluation for general visual representations hinders progress. Popularprotocols are often too constrained (linear classification), limited indiversity (ImageNet, CIFAR, Pascal-VOC), or only weakly related torepresentation quality (ELBO, reconstruction error). We present the Visual TaskAdaptation Benchmark (VTAB), which defines good representations as those thatadapt to diverse, unseen tasks with few examples. With VTAB, we conduct alarge-scale study of many popular publicly-available representation learningalgorithms. We carefully control confounders such as architecture and tuningbudget. We address questions like: How effective are ImageNet representationsbeyond standard natural datasets? How do representations trained via generativeand discriminative models compare? To what extent can self-supervision replacelabels? And, how close are we to general visual representations?

A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark