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

• URL: http://arxiv.org/abs/2107.04952v2
• Date: Wed, 14 Jul 2021 01:28:32 GMT
• Title: Learn from Anywhere: Rethinking Generalized Zero-Shot Learning with Limited Supervision
• Authors: Gaurav Bhatt, Shivam Chandhok and Vineeth N Balasubramanian
• Abstract summary: We present a practical setting of inductive zero and few-shot learning, where unlabeled images from other out-of-data classes can be used to improve generalization. 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.
• Score: 16.12500804569801
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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