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In the absence of large labelled datasets, self-supervised learningtechniques can boost performance by learning useful representations fromunlabelled data, which is often more readily available. However, there is oftena domain shift between the unlabelled collection and the downstream targetproblem data. We show that by learning Bayesian instance weights for theunlabelled data, we can improve the downstream classification accuracy byprioritising the most useful instances. Additionally, we show that the trainingtime can be reduced by discarding unnecessary datapoints. Our method,BetaDataWeighter is evaluated using the popular self-supervised rotationprediction task on STL-10 and Visual Decathlon. We compare to related instanceweighting schemes, both hand-designed heuristics and meta-learning, as well asconventional self-supervised learning. BetaDataWeighter achieves both thehighest average accuracy and rank across datasets, and on STL-10 it prunes upto 78% of unlabelled images without significant loss in accuracy, correspondingto over 50% reduction in training time.

Don't Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights