Exploring the Landscape of Spatial Robustness
Authors: Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform extensive experiments that provide a fine-grained understanding of rotation / translation robustness on a wide spectrum of datasets and training regimes. |
| Researcher Affiliation | Academia | 1EECS, MIT, Massachusetts, USA. Correspondence to: Logan Engstrom <engstrom@mit.edu>, Brandon Tran <btran115@mit.edu>, Dimitris Tsipras <tsipras@mit.edu>, Ludwig Schmidt <ludwigs@mit.edu>, Aleksander M adry <madry@mit.edu>. |
| Pseudocode | No | No pseudocode or algorithm blocks found. |
| Open Source Code | No | No explicit statement or link providing access to the authors' own open-source code for the methodology described in the paper. |
| Open Datasets | Yes | We evaluate standard image classifiers for the MNIST (Le Cun et al., 1998), CIFAR10 (Krizhevsky & Hinton, 2009) and Image Net (Russakovsky et al., 2015) datasets. |
| Dataset Splits | No | The paper mentions training on MNIST, CIFAR10, and ImageNet datasets, and evaluates on their test sets, but does not provide explicit training/validation/test dataset splits (e.g., percentages or sample counts) used in their experiments. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments are provided in the paper. |
| Software Dependencies | No | The paper mentions using TensorFlow and Tensorpack, but does not specify version numbers for these or other software dependencies. |
| Experiment Setup | Yes | For grid search attacks, we consider 5 values per translation direction and 31 values for rotations, equally spaced. For first-order attacks, we use 200 steps of projected gradient descent of step size 0.01 times the parameter range. |