  Zero-shot learning (ZSL) refers to the problem of learning to classify
instances from the novel classes (unseen) that are absent in the training set
(seen). Most ZSL methods infer the correlation between visual features and
attributes to train the classifier for unseen classes. However, such models may
have a strong bias towards seen classes during training. Meta-learning has been
introduced to mitigate the basis, but meta-ZSL methods are inapplicable when
tasks used for training are sampled from diverse distributions. In this regard,
we propose a novel Task-aligned Generative Meta-learning model for Zero-shot
learning (TGMZ). TGMZ mitigates the potentially biased training and enables
meta-ZSL to accommodate real-world datasets containing diverse distributions.
TGMZ incorporates an attribute-conditioned task-wise distribution alignment
network that projects tasks into a unified distribution to deliver an unbiased
model. Our comparisons with state-of-the-art algorithms show the improvements
of 2.1%, 3.0%, 2.5%, and 7.6% achieved by TGMZ on AWA1, AWA2, CUB, and aPY
datasets, respectively. TGMZ also outperforms competitors by 3.6% in
generalized zero-shot learning (GZSL) setting and 7.9% in our proposed
fusion-ZSL setting.
