  Learning to learn plays a pivotal role in meta-learning (MTL) to obtain an
optimal learning model. In this paper, we investigate mage recognition for
unseen categories of a given dataset with limited training information. We
deploy a zero-shot learning (ZSL) algorithm to achieve this goal. We also
explore the effect of parameter tuning on performance of semantic auto-encoder
(SAE). We further address the parameter tuning problem for meta-learning,
especially focusing on zero-shot learning. By combining different embedded
parameters, we improved the accuracy of tuned-SAE. Advantages and disadvantages
of parameter tuning and its application in image classification are also
explored.
