Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity
Authors: Andrew Cullen, Paul Montague, Shijie Liu, Sarah Erfani, Benjamin Rubinstein
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate the performance of our proposed certification improvements, we considered the certified radius produced for MNIST [19], CIFAR-10 [18], and Tiny-Imagenet [16], the latter of these is a 200-class variant of Imagenet [43] which downsamples images to 3 60 60. |
| Researcher Affiliation | Collaboration | Andrew C. Cullen1 Paul Montague2 Shijie Liu1 Sarah M. Erfani1 Benjamin I.P. Rubinstein1 1School of Computing and Information Systems, University of Melbourne, Parkville, Australia 2Defence Science and Technology Group, Adelaide, Australia |
| Pseudocode | Yes | Algorithm 1 Single Bubble Loop. |
| Open Source Code | Yes | The full code to implement our experiments can be found at https://github.com/andrew-cullen/DoubleBubble. |
| Open Datasets | Yes | To evaluate the performance of our proposed certification improvements, we considered the certified radius produced for MNIST [19], CIFAR-10 [18], and Tiny-Imagenet [16] |
| Dataset Splits | No | The paper states training details like 'Training employed Cross Entropy loss with a batch size of 128 over 50 epochs' and 'Training occurred using SGD over 80 epochs', but it does not specify explicit training/validation/test dataset splits (e.g., 80/10/10 percentages or sample counts for each split). |
| Hardware Specification | Yes | For both MNIST and CIFAR-10, our experimentation utilised a single NVIDIA P100 GPU core with 12 GB of GPU RAM... Tiny-Imagenet training and evaluation utilised 3 P100 GPU s |
| Software Dependencies | No | The paper states that 'All datasets were modelled using the Resnet18 architecture in Py Torch [32]', but it does not specify the version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Training employed Cross Entropy loss with a batch size of 128 over 50 epochs... Parameter optimisation was performed with Adam [17], with the learning rate set as 0.001. ...Training occurred using SGD over 80 epochs, with a starting learning rate of 0.1, decreasing by a factor of 10 after 30 and 60 epochs, and momentum set to 0.9. |