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OT-CAL: Optimal-Transport-Guided Adaptive Sample Selection for Efficient Visual Learning
Deep neural networks for image recognition are typically trained using mini-batches, with a forward pass to compute predictions and losses followed by a more computationally expensive backward pass to update model parameters. However, not all training samples contribute equally to learning, suggesti
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