Robust Models are less Over-Confident
Authors: Julia Grabinski, Paul Gavrikov, Janis Keuper, Margret Keuper
NeurIPS 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we empirically analyze a variety of adversarially trained models that achieve high robust accuracies when facing state-of-the-art attacks and we show that AT has an interesting side-effect: it leads to models that are significantly less overconfident with their decisions, even on clean data than non-robust models. Our experiments for 71 robust and non-robust model pairs on the datasets CIFAR10 [43], CIFAR100 and Image Net [19] confirm that non-robust models are overconfident with their false predictions. |
| Researcher Affiliation | Academia | Julia Grabinski Fraunhofer ITWM, Kaiserslautern Visual Computing, University of Siegen EMAIL Paul Gavrikov IMLA, Offenburg University Janis Keuper Fraunhofer ITWM, Kaiserslautern IMLA, Offenburg University Margret Keuper University of Siegen Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Data & Project website: https://github.com/Ge Julia/robustness_ confidences_evaluation |
| Open Datasets | Yes | Our experiments for 71 robust and non-robust model pairs on the datasets CIFAR10 [43], CIFAR100 and Image Net [19] confirm that non-robust models are overconfident with their false predictions. |
| Dataset Splits | Yes | CIFAR10 [43] is a simple ten class dataset consisting of 50,000 training and 10,000 validation images with a resolution of 32 32. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper does not provide specific details about software dependencies, including version numbers for libraries or frameworks used. |
| Experiment Setup | Yes | Training details can be found in appendix A. |