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..
A Fourier Perspective on Model Robustness in Computer Vision
Authors: Dong Yin, Raphael Gontijo Lopes, Jon Shlens, Ekin Dogus Cubuk, Justin Gilmer
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For our experiments on CIFAR-10, we use the Wide Res Net-28-10 architecture [27], and for our experiment on Image Net, we use the Res Net-50 architecture [16]. When we use Gaussin data augmentation, we choose parameter σ = 0.1 for CIFAR-10 and σ = 0.4 for Image Net. All experiments use flip and crop during training. |
| Researcher Affiliation | Collaboration | Dong Yin Department of EECS UC Berkeley Berkeley, CA 94720 EMAIL; Raphael Gontijo Lopes Google Research, Brain team Mountain View, CA 94043 EMAIL; Jonathon Shlens Google Research, Brain team Mountain View, CA 94043 EMAIL; Ekin D. Cubuk Google Research, Brain team Mountain View, CA 94043 EMAIL; Justin Gilmer Google Research, Brain team Mountain View, CA 94043 EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper states: 'Our experiment is based on the open source implementation of Auto Augment at https://github.com/tensorflow/models/tree/master/research/autoaugment.' This refers to a third-party open-source implementation used by the authors, not the authors' own code for their specific methodology. |
| Open Datasets | Yes | For our experiments on CIFAR-10, we use the Wide Res Net-28-10 architecture [27], and for our experiment on Image Net, we use the Res Net-50 architecture [16].; achieves state-of-the-art robustness on the CIFAR-10-C [17] benchmark.; As for the Image Net-C benchmark... |
| Dataset Splits | Yes | Given a model and a validation image X, we can generate a perturbed image with Fourier basis noise.; Error rates are averaged over the entire Image Net validation set.; Error rates are averaged over 1000 randomly sampled images from the test set. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or cloud computing instance specifications used for running its experiments. |
| Software Dependencies | No | The paper mentions 'tensorflow' indirectly via a GitHub link for Auto Augment, but does not provide specific version numbers for TensorFlow or any other software dependencies, nor does it list multiple key components with their versions. |
| Experiment Setup | Yes | When we use Gaussin data augmentation, we choose parameter σ = 0.1 for CIFAR-10 and σ = 0.4 for Image Net. All experiments use flip and crop during training. |