  A common problem with most zero and few-shot learning approaches is they
suffer from bias towards seen classes resulting in sub-optimal performance.
Existing efforts aim to utilize unlabeled images from unseen classes (i.e
transductive zero-shot) during training to enable generalization. However, this
limits their use in practical scenarios where data from target unseen classes
is unavailable or infeasible to collect. In this work, we present a practical
setting of inductive zero and few-shot learning, where unlabeled images from
other out-of-data classes, that do not belong to seen or unseen categories, can
be used to improve generalization in any-shot learning. We leverage a
formulation based on product-of-experts and introduce a new AUD module that
enables us to use unlabeled samples from out-of-data classes which are usually
easily available and practically entail no annotation cost. In addition, we
also demonstrate the applicability of our model to address a more practical and
challenging, Generalized Zero-shot under a limited supervision setting, where
even base seen classes do not have sufficient annotated samples.
