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arXiv · 2003.00837

On Parameter Tuning in Meta-learning for Computer Vision

Farid Ghareh Mohammadi·M. Hadi Amini·Hamid R. Arabnia
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Abstract

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.

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Farid Ghareh Mohammadi, M. Hadi Amini, Hamid R. Arabnia. 2020-02-11. On Parameter Tuning in Meta-learning for Computer Vision. https://arxiv.org/abs/2003.00837

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