论文标题
Robustar：交互式工具箱支持可靠视力学习的精确数据注释
Robustar: Interactive Toolbox Supporting Precise Data Annotation for Robust Vision Learning
论文作者
论文摘要
我们介绍了软件Robustar的初步发布，该版本旨在通过数据驱动的视角提高视觉分类机器学习模型的鲁棒性。基于最近的理解，即缺乏机器学习模型的鲁棒性是该模型学习虚假特征的趋势，我们旨在通过在培训前从数据角度删除数据的数据，从而从数据角度解决此问题。特别是，我们介绍了一种软件，该软件可以帮助用户通过允许用户注释图像的像素级别的虚假功能来更好地为训练图像分类模型准备数据。为了促进这一过程，我们的软件还利用了最近的进步来帮助识别值得关注的潜在图像和像素，并通过新注释的数据继续进行培训。我们的软件托管在GitHub存储库https://github.com/haohanwang/robustar。
We introduce the initial release of our software Robustar, which aims to improve the robustness of vision classification machine learning models through a data-driven perspective. Building upon the recent understanding that the lack of machine learning model's robustness is the tendency of the model's learning of spurious features, we aim to solve this problem from its root at the data perspective by removing the spurious features from the data before training. In particular, we introduce a software that helps the users to better prepare the data for training image classification models by allowing the users to annotate the spurious features at the pixel level of images. To facilitate this process, our software also leverages recent advances to help identify potential images and pixels worthy of attention and to continue the training with newly annotated data. Our software is hosted at the GitHub Repository https://github.com/HaohanWang/Robustar.
