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arXiv · 2208.08132

Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning

Dung Anh Hoang·Cuong Nguyen·Belagiannis Vasileios·Thanh-Toan Do·Gustavo Carneiro
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Abstract

Meta-learning is an effective method to handle imbalanced and noisy-label learning, but it depends on a validation set containing randomly selected, manually labelled and balanced distributed samples. The random selection and manual labelling and balancing of this validation set is not only sub-optimal for meta-learning, but it also scales poorly with the number of classes. Hence, recent meta-learning papers have proposed ad-hoc heuristics to automatically build and label this validation set, but these heuristics are still sub-optimal for 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. Furthermore, we propose a new imbalanced noisy-label meta-learning (INOLML) algorithm that automatically builds a validation set by maximising its utility using the criteria above. Our method shows significant improvements over previous meta-learning approaches and sets the new state-of-the-art on several benchmarks.

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Dung Anh Hoang, Cuong Nguyen, Belagiannis Vasileios, Thanh-Toan Do, Gustavo Carneiro. 2022-08-17. Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning. https://arxiv.org/abs/2208.08132

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