A Fourier Perspective on Model Robustness in Computer Vision
Authors: Dong Yin, Raphael Gontijo Lopes, Jon Shlens, Ekin Dogus Cubuk, Justin Gilmer
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 dongyin@berkeley.edu; Raphael Gontijo Lopes Google Research, Brain team Mountain View, CA 94043 iraphael@google.com; Jonathon Shlens Google Research, Brain team Mountain View, CA 94043 shlens@google.com; Ekin D. Cubuk Google Research, Brain team Mountain View, CA 94043 cubuk@google.com; Justin Gilmer Google Research, Brain team Mountain View, CA 94043 gilmer@google.com |
| 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. |