Abstract:
Zero shot learning (ZSL) aims to recognize unseen
classes by exploiting semantic relationships between seen
and unseen classes. Two major problems faced by ZSL algorithms are the hubness problem and the bias towards the
seen classes. Existing ZSL methods focus on only one of
these problems in the conventional and generalized ZSL setting. In this work, we propose a novel approach, Semantically Aligned Bias Reducing (SABR) ZSL, which focuses on
solving both the problems. It overcomes the hubness problem by learning a latent space that preserves the semantic
relationship between the labels while encoding the discriminating information about the classes. Further, we also propose ways to reduce bias of the seen classes through a simple cross-validation process in the inductive setting and a
novel weak transfer constraint in the transductive setting.
Extensive experiments on three benchmark datasets suggest
that the proposed model significantly outperforms existing
state-of-the-art algorithms by ∼1.5-9% in the conventional
ZSL setting and by ∼2-14% in the generalized ZSL for both
the inductive and transductive settings.
