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

• URL: http://arxiv.org/abs/2105.05736v1
• Date: Wed, 12 May 2021 15:40:13 GMT
• Title: Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces
• Authors: Ankit Singh Rawat, Aditya Krishna Menon, Wittawat Jitkrittum, Sadeep Jayasumana, Felix X. Yu, Sashank Reddi, Sanjiv Kumar
• Abstract summary: We show that different negative sampling schemes implicitly trade-off performance on dominant versus rare labels. 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.
• Score: 64.23172847182109
• License: http://creativecommons.org/licenses/by/4.0/
• 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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