论文标题
AutoCl：一种基于模型性能的自动深度学习分类器推荐的视觉交互系统
AutoCl : A Visual Interactive System for Automatic Deep Learning Classifier Recommendation Based on Models Performance
论文作者
论文摘要
如今，深度学习（DL）模型越来越多地应用于各个领域，没有技术专业知识的人和领域知识努力为其任务找到适当的模型。在本文中，我们介绍了AutoCl视觉交互式推荐系统，旨在帮助非专家采用合适的DL分类器。我们的系统使用户可以比较经过各种超参数设置训练的多个分类器的性能和行为，并自动推荐具有适当的超参数的最佳分类器。我们将AutoCL的功能与最近的几个Automl系统进行了比较，并表明它可以更好地选择DL分类器。最后，我们使用公开可用数据集演示了用于图像分类的用例，以显示我们系统的功能。
Nowadays, deep learning (DL) models being increasingly applied to various fields, people without technical expertise and domain knowledge struggle to find an appropriate model for their task. In this paper, we introduce AutoCl a visual interactive recommender system aimed at helping non-experts to adopt an appropriate DL classifier. Our system enables users to compare the performance and behavior of multiple classifiers trained with various hyperparameter setups as well as automatically recommends a best classifier with appropriate hyperparameter. We compare features of AutoCl against several recent AutoML systems and show that it helps non-experts better in choosing DL classifier. Finally, we demonstrate use cases for image classification using publicly available dataset to show the capability of our system.
