Disentangling Sampling and Labeling Bias for Learning in Large-output Spaces
Ankit Singh Rawat,u00a0Aditya K Menon,u00a0Wittawat Jitkrittum,u00a0Sadeep Jayasumana,u00a0Felix Yu,u00a0Sashank Reddi,u00a0Sanjiv Kumar
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.
