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We introduce the initial release of our software Robustar, which aims toimprove the robustness of vision classification machine learning models througha data-driven perspective. Building upon the recent understanding that the lackof machine learning model's robustness is the tendency of the model's learningof spurious features, we aim to solve this problem from its root at the dataperspective by removing the spurious features from the data before training. Inparticular, we introduce a software that helps the users to better prepare thedata for training image classification models by allowing the users to annotatethe spurious features at the pixel level of images. To facilitate this process,our software also leverages recent advances to help identify potential imagesand pixels worthy of attention and to continue the training with newlyannotated data. Our software is hosted at the GitHub Repositoryhttps://github.com/HaohanWang/Robustar.

Robustar: Interactive Toolbox Supporting Precise Data Annotation for Robust Vision Learning