DeformRS: Certifying Input Deformations with Randomized Smoothing
Authors: Motasem Alfarra, Adel Bibi, Naeemullah Khan, Philip H.S. Torr, Bernard Ghanem6001-6009
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on MNIST, CIFAR10, and Image Net show competitive performance of DEFORMRS-PAR achieving a certified accuracy of 39% against perturbed rotations in the set [ 10 , 10 ] on Image Net. |
| Researcher Affiliation | Academia | 1 King Abdullah University of Science and Technology (KAUST), 2 University of Oxford |
| Pseudocode | No | The paper contains mathematical definitions and theorems but no explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Official Code: https://github.com/MotasemAlfarra/DeformRS. |
| Open Datasets | Yes | Setup. We follow standard practices prior art, e.g. LI and FBV, and conduct experiments on MNIST (Le Cun 1998), CIFAR10 (Krizhevsky 2012), and Image Net (Russakovsky et al. 2015) datasets. |
| Dataset Splits | No | The paper mentions a 'test set' for evaluation and 'cross-validated over λ' for reporting results but does not provide specific details on training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | Yes | In all of our training experiments, we used a single NVIDIA 1080-TI for CIFAR10 and MNIST experiments while we used 2 NVIDIA V100 to fine tune Image Net models. For the certification experiments, we use a single GPU per experiment (NVIDIA 1080-TI for CIFAR10 and MNIST and NVIDIA V100 for Image Ne). |
| Software Dependencies | No | The paper mentions using 'publicly available code (Cohen, Rosenfeld, and Kolter 2019)' but does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | For experiments on MNIST and CIFAR10, we certify Res Net18 (He et al. 2016) trained for 90 epochs with a learning rate of 0.1, momentum of 0.9, weight decay of 10 4, and learning rate decay at epochs 30 and 60 by a factor of 0.1. For Image Net experiments, we certify a fine-tuned pretrained Res Net50 for 30 epochs using SGD with a learning rate of 10 3 that decays at every 10 epochs by a factor of 0.1. |