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..
Exploring the Landscape of Spatial Robustness
Authors: Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry
ICML 2019 | Venue PDF | 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 <EMAIL>, Brandon Tran <EMAIL>, Dimitris Tsipras <EMAIL>, Ludwig Schmidt <EMAIL>, Aleksander M adry <EMAIL>. |
| 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. |