mlsanityData to 7 Oct 2026

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

arXiv 1901.11409 ↗First posted 29 Jan 2019cs.CV cs.LG stat.MLcited by 43

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Abstract (arXiv)

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

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jan 2019.

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Citing papers per year: 2021: 5; 2022: 10; 2023: 15; 2024: 7; 2025: 3; 2026: 3.

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