  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.
