Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Unadversarial Examples: Designing Objects for Robust Vision
Authors: Hadi Salman, Andrew Ilyas, Logan Engstrom, Sai Vemprala, Aleksander Madry, Ashish Kapoor
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our framework yields improved performance on standard benchmarks, a simulated robotics environment, and physical-world experiments. |
| Researcher Affiliation | Collaboration | Hadi Salman EMAIL MIT Andrew Ilyas EMAIL MIT Logan Engstrom EMAIL MIT Sai Vemprala EMAIL Microsoft Research Aleksander M adry EMAIL MIT Ashish Kapoor EMAIL Microsoft Research |
| Pseudocode | Yes | Unadversarial patches (cf. Algorithm 1 in Appendix A). Unadversarial textures (cf. Algorithm 2 in Appendix A). |
| Open Source Code | Yes | 1Our code is available at https://github.com/microsoft/unadversarial. The code link is in the abstract and Appendix C.6. |
| Open Datasets | Yes | Using the algorithm described in Section 2.3, we construct unadversarial patches of varying size for pre-trained Res Net-50 classifiers on the CIFAR [Kri09] and Image Net [RDS+15] datasets. |
| Dataset Splits | Yes | For evaluation, we add these patches at random positions, scales, and orientations to validation set images (see Appendix C for the exact protocol). We collect meshes corresponding to four Image Net classes: warplane, minibus, container ship, and trailer truck, from sketchfab.com. We generate a texture for each object using the unadversarial texture algorithm of Section 2.3, using the Image Net validation set as the set of backgrounds for the algorithm, and a pre-trained Res Net-50 as the classifier. |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See Appendix C.5. |
| Software Dependencies | No | The paper mentions using 'Mitsuba' as a 3D renderer but does not provide specific version numbers for this or any other software dependencies. |
| Experiment Setup | Yes | For a more detailed account of each experimental setup, see Appendix C. Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Appendix C. |