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Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets
Yogesh Balaji, Tom Goldstein, Judy Hoffman
Adversarial training is by far the most successful strategy for improving robustness of neural networks to adversarial attacks. Despite its success as a defense mechanism, adversar…
Robust Learning with Jacobian Regularization
Judy Hoffman, Daniel A. Roberts, Sho Yaida
Design of reliable systems must guarantee stability against input perturbations. In machine learning, such guarantee entails preventing overfitting and ensuring robustness of model…
SplitNet: Sim2Sim and Task2Task Transfer for Embodied Visual Navigation
Daniel Gordon, Abhishek Kadian, Devi Parikh +2
We propose SplitNet, a method for decoupling visual perception and policy learning. By incorporating auxiliary tasks and selective learning of portions of the model, we explicitly…
Predictive Inequity in Object Detection
Benjamin Wilson, Judy Hoffman, Jamie Morgenstern
In this work, we investigate whether state-of-the-art object detection systems have equitable predictive performance on pedestrians with different skin tones. This work is motivate…