Robust Models are less Over-Confident
Authors: Julia Grabinski, Paul Gavrikov, Janis Keuper, Margret Keuper
NeurIPS 2022 | Conference 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 julia.grabinski@itwm.fraunhofer.de 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. |