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A common problem with most zero and few-shot learning approaches is theysuffer from bias towards seen classes resulting in sub-optimal performance.Existing efforts aim to utilize unlabeled images from unseen classes (i.etransductive zero-shot) during training to enable generalization. However, thislimits their use in practical scenarios where data from target unseen classesis unavailable or infeasible to collect. In this work, we present a practicalsetting of inductive zero and few-shot learning, where unlabeled images fromother out-of-data classes, that do not belong to seen or unseen categories, canbe used to improve generalization in any-shot learning. We leverage aformulation based on product-of-experts and introduce a new AUD module thatenables us to use unlabeled samples from out-of-data classes which are usuallyeasily available and practically entail no annotation cost. In addition, wealso demonstrate the applicability of our model to address a more practical andchallenging, Generalized Zero-shot under a limited supervision setting, whereeven base seen classes do not have sufficient annotated samples.

Learn from Anywhere: Rethinking Generalized Zero-Shot Learning with Limited Supervision