Long-tail Mixup for Extreme Multi-label Classification

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Extreme multi-label classification (XMC) aims at finding multiple relevant labels for a given sample from a huge label set at the industrial scale. The XMC problem inherently poses two challenges: scalability and label sparsity - the number of labels is too large, and labels follow the long-tail distribution. To resolve these problems, we propose a novel Mixup-based augmentation method for long-tail labels, called TailMix. Building upon the partition-based model, TailMix utilizes the context vectors generated from the label attention layer. It first selectively chooses two context vectors using the inverse propensity score of labels and the label proximity graph representing the co-occurrence of labels. Using two context vectors, it augments new samples with the long-tail label to improve the accuracy of long-tail labels. Despite its simplicity, experimental results show that TailMix consistently outperforms other augmentation methods on three benchmark datasets, especially for long-tail labels in terms of two metrics, PSP@k and PSN@k.
Publisher
Association for Computing Machinery
Issue Date
2022-10
Language
English
Citation

31st ACM International Conference on Information and Knowledge Management, CIKM 2022, pp.3998 - 4002

DOI
10.1145/3511808.3557632
URI
http://hdl.handle.net/10203/312535
Appears in Collection
AI-Conference Papers(학술대회논문)
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