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arXiv · 2105.05736

Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces

Ankit Singh Rawat·Aditya Krishna Menon·Wittawat Jitkrittum·Sadeep Jayasumana·Felix X. Yu·Sashank Reddi·Sanjiv Kumar
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Abstract

Negative sampling schemes enable efficient training given a large number of classes, by offering a means to approximate a computationally expensive loss function that takes all labels into account. In this paper, we present a new connection between these schemes and loss modification techniques for countering label imbalance. We show that different negative sampling schemes implicitly trade-off performance on dominant versus rare labels. Further, we provide a unified means to explicitly tackle both sampling bias, arising from working with a subset of all labels, and labeling bias, which is inherent to the data due to label imbalance. We empirically verify our findings on long-tail classification and retrieval benchmarks.

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Ankit Singh Rawat, Aditya Krishna Menon, Wittawat Jitkrittum, Sadeep Jayasumana, Felix X. Yu, Sashank Reddi, Sanjiv Kumar. 2021-05-12. Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces. https://arxiv.org/abs/2105.05736

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