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Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning
In this paper, we analyse the meta-learning algorithm and propose new criteria to characterise the utility of the validation set, based on: 1) the informativeness of the validation set; 2) the class distribution balance of the set; and 3) the correctness of the labels of the set.
A Spacecraft Dataset for Detection, Segmentation and Parts Recognition
We also provide evaluations with state-of-the-art methods in object detection and instance segmentation as a benchmark for the dataset.
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