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Large datasets have been crucial to the success of deep learning models inthe recent years, which keep performing better as they are trained with morelabelled data. While there have been sustained efforts to make these modelsmore data-efficient, the potential benefit of understanding the data itself, islargely untapped. Specifically, focusing on object recognition tasks, we wonderif for common benchmark datasets we can do better than random subsets of thedata and find a subset that can generalize on par with the full dataset whentrained on. To our knowledge, this is the first result that can find notableredundancies in CIFAR-10 and ImageNet datasets (at least 10%). Interestingly,we observe semantic correlations between required and redundant images. We hopethat our findings can motivate further research into identifying additionalredundancies and exploiting them for more efficient training ordata-collection.

Semantic Redundancies in Image-Classification Datasets: The 10% You Don't Need