May 12, 2021
Debuggable Deep Networks: Sparse Linear Models (Part 1)
We show how fitting sparse linear models over learned deep feature representations can lead to more debuggable neural networks while remaining highly accurate.
Dec 22, 2020
Unadversarial Examples: Designing Objects for Robust Vision
We show how to design objects to help, rather than hurt, the performance of vision systems; the resulting objects improve performance on natural and distribution-shifted data.
Aug 12, 2020
Benchmarks for Subpopulation Shift
We develop a methodology for constructing large-scale benchmarks to assess the robustness of standard models to subpopulation shift.
Jul 20, 2020
Transfer Learning with Adversarially Robust Models
We find that adversarially robust neural networks are better for downstream transfer learning than standard networks, despite having lower accuracy.
Jun 18, 2020
Noise or Signal: The Role of Backgrounds in Image Classification
To what extent to state-of-the-art vision models depend on image backgrounds?
May 25, 2020
From ImageNet to Image Classification
We take a closer look at the ImageNet dataset and identify ways in which it deviates from the underlying object recognition task.
May 19, 2020
Identifying Statistical Bias in Dataset Replication
Statistical bias in dataset reproduction studies can lead to skewed outcomes and observations.
Jun 6, 2019
Robustness Beyond Security: Computer Vision Applications
An off-the-shelf robust classifier can be used to perform a range of computer vision tasks beyond classification.