# On Parameter Tuning in Meta-learning for Computer Vision

arXiv: https://arxiv.org/abs/2003.00837 · HTML: https://mlsanity.com/paper/2003.00837/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2020.

## Built on by (most cited first, 6 of 6)

- [A Concise Review of Transfer Learning](https://mlsanity.com/paper/2104.02144/) (2021)
- [Search Algorithms for Automated Hyper-Parameter Tuning](https://mlsanity.com/paper/2104.14677/) (2021)
- [Data Analytics for Smart cities: Challenges and Promises](https://mlsanity.com/paper/2109.05581/) (2021)
- [HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization](https://mlsanity.com/paper/2109.05319/) (2021)
- [An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization](https://mlsanity.com/paper/2504.06207/) (2025)
- [3D-model ShapeNet Core Classification using Meta-Semantic Learning](https://mlsanity.com/paper/2205.15869/) (2022)

## Abstract (arXiv)

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.