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The best quantitative models of these areas are deep neural networks trained with human annotations. However, they receive more annotations than infants, making them implausible models of the ventral stream development. Here, we report that recent progress in unsupervised learning has largely closed this gap. We find the networks learned with recent unsupervised methods achieve prediction accuracy in the ventral stream that equals or exceeds that of today\u2019s best models. 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