EasyRobust: A Comprehensive and Easy-to-use Toolkit for Robust and Generalized Vision
This toolkit addresses robustness issues in computer vision for researchers and practitioners, but it is incremental as it builds on existing methods without introducing new paradigms.
The authors tackled the problem of deep neural networks lacking robustness in computer vision by developing EasyRobust, a toolkit for training and evaluating models against adversarial attacks and data distribution shifts, resulting in a comprehensive open-source resource with benchmarks for accurate robustness assessment.
Deep neural networks (DNNs) has shown great promise in computer vision tasks. However, machine vision achieved by DNNs cannot be as robust as human perception. Adversarial attacks and data distribution shifts have been known as two major scenarios which degrade machine performance and obstacle the wide deployment of machines "in the wild". In order to break these obstructions and facilitate the research of model robustness, we develop EasyRobust, a comprehensive and easy-to-use toolkit for training, evaluation and analysis of robust vision models. EasyRobust targets at two types of robustness: 1) Adversarial robustness enables the model to defense against malicious inputs crafted by worst-case perturbations, also known as adversarial examples; 2) Non-adversarial robustness enhances the model performance on natural test images with corruptions or distribution shifts. Thorough benchmarks on image classification enable EasyRobust to provide an accurate robustness evaluation on vision models. We wish our EasyRobust can help for training practically-robust models and promote academic and industrial progress in closing the gap between human and machine vision. Codes and models of EasyRobust have been open-sourced in https://github.com/alibaba/easyrobust.