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arXiv · 2103.01284

Performance Variability in Zero-Shot Classification

Matías Molina·Jorge Sánchez
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Abstract

Zero-shot classification (ZSC) is the task of learning predictors for classes not seen during training. Although the different methods in the literature are evaluated using the same class splits, little is known about their stability under different class partitions. In this work we show experimentally that ZSC performance exhibits strong variability under changing training setups. We propose the use ensemble learning as an attempt to mitigate this phenomena.

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Matías Molina, Jorge Sánchez. 2021-03-01. Performance Variability in Zero-Shot Classification. https://arxiv.org/abs/2103.01284

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