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Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding
Power-law scaling indicates that large-scale training with uniform sampling is prohibitively slow.
Divide and Contrast: Self-supervised Learning from Uncurated Data
Self-supervised learning holds promise in leveraging large amounts of unlabeled data, however much of its progress has thus far been limited to highly curated pre-training data such as ImageNet.
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