论文标题
在计算机视觉的元学习中进行参数调整
On Parameter Tuning in Meta-learning for Computer Vision
论文作者
论文摘要
学习学习在元学习（MTL）中起着关键作用，以获得最佳的学习模型。在本文中，我们调查了具有有限培训信息的给定数据集的看不见类别的法师识别。我们部署了零拍学习（ZSL）算法来实现此目标。我们还探讨了参数调整对语义自动编码器（SAE）性能的影响。我们进一步解决了用于元学习的参数调整问题，尤其是专注于零拍学习。通过组合不同的嵌入式参数，我们提高了调谐的sae准确性。还探讨了参数调整的优势和缺点及其在图像分类中的应用。
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.
