论文标题
Kaggle亲属识别挑战挑战：引入无卷积模型以增强常规
Kaggle Kinship Recognition Challenge: Introduction of Convolution-Free Model to boost conventional
论文作者
论文摘要
这项工作旨在探索无卷积的基本分类器，该分类器可用于扩大常规合奏分类器的变化。具体而言，我们建议视觉变压器作为基本分类器，以与CNN结合使用Kaggle亲属识别中的独特集合解决方案。在本文中，我们通过在现有CNN模型之上实施和优化视觉变压器模型的变体来验证我们的想法。组合模型比仅基于CNN变体的常规集合分类器获得更好的分数。我们证明，高度优化的CNN合奏在Kaggle讨论委员会上公开可用，可以通过与Vision Transformer模型的变体简单地与Vision Transformer模型的变体进行显着提高ROC得分，这是由于低相关性的。
This work aims to explore a convolution-free base classifier that can be used to widen the variations of the conventional ensemble classifier. Specifically, we propose Vision Transformers as base classifiers to combine with CNNs for a unique ensemble solution in Kaggle kinship recognition. In this paper, we verify our proposed idea by implementing and optimizing variants of the Vision Transformer model on top of the existing CNN models. The combined models achieve better scores than conventional ensemble classifiers based solely on CNN variants. We demonstrate that highly optimized CNN ensembles publicly available on the Kaggle Discussion board can easily achieve a significant boost in ROC score by simply ensemble with variants of the Vision Transformer model due to low correlation.
