# Image Classification on CIFAR-10 (Noise Robustness) Source: [Image Classification on CIFAR-10 (Noise Robustness)]() Collection index: [llms.txt]() Data updated: 2026-04-14T10:34:32.707Z Evaluation of Image Classification performance on the CIFAR-10 dataset reporting accuracy under various common noise and corruption conditions (e.g., Gaussian, Motion, Contrast). A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. | Scope | Recorded value | | --- | --- | | Task | Image Classification | | Dataset (including any stated split) | CIFAR-10 | | Metric | Accuracy (Original) | | Unit | PERCENT | | Direction | Higher is better | | Results loaded | 7 of 7 | | Coverage | All results currently returned for this benchmark | Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol. ## Metrics - [Accuracy (Original)](): Higher is better; unit: PERCENT. - [Accuracy (Gaussian Noise)](): Higher is better; unit: PERCENT. - [Accuracy (Glass)](): Higher is better; unit: PERCENT. - [Accuracy (Shot Noise)](): Higher is better; unit: PERCENT. - [Accuracy (Impulse Noise)](): Higher is better; unit: PERCENT. - [Accuracy (Speckle Noise)](): Higher is better; unit: PERCENT. - [Accuracy (Motion Blur)](): Higher is better; unit: PERCENT. - [Accuracy (Brightness)](): Higher is better; unit: PERCENT. - [Accuracy (Contrast)](): Higher is better; unit: PERCENT. - [Average Accuracy (Corruptions)](): Higher is better; unit: PERCENT. ## Recorded results | Method | Accuracy (Original) | Result date | Report paper | | --- | --- | --- | --- | | Neural ODE | 91.45 | Not recorded | 68fa5e551e209131ce9e165d | | Zubov-Net | 91.29 | Not recorded | 68fa5e551e209131ce9e165d | | ResNet18 | 91.11 | Not recorded | 68fa5e551e209131ce9e165d | | SODEF | 90.19 | Not recorded | 68fa5e551e209131ce9e165d | | Proj-NODE | 90.03 | Not recorded | 68fa5e551e209131ce9e165d | | FxTS-Net | 88.52 | Not recorded | 68fa5e551e209131ce9e165d | | LyaNet | 88.34 | Not recorded | 68fa5e551e209131ce9e165d | ### Neural ODE Accuracy (Original): 91.45. Result ID: 69de183147f34ce924c63c99. [Reporting paper]() [Method origin paper]() [Implementation]() Recorded evaluation configuration: {"Activation":"Tanh"}. This record does not assert independent reproduction. ### Zubov-Net Accuracy (Original): 91.29. Result ID: 69de183147f34ce924c63c9e. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: {"Activation":"Tanh"}. This record does not assert independent reproduction. ### ResNet18 Accuracy (Original): 91.11. Result ID: 69de183147f34ce924c63c98. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: Not recorded. This record does not assert independent reproduction. ### SODEF Accuracy (Original): 90.19. Result ID: 69de183147f34ce924c63c9b. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: {"Activation":"sin"}. This record does not assert independent reproduction. ### Proj-NODE Accuracy (Original): 90.03. Result ID: 69de183147f34ce924c63c9a. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: {"Activation":"Tanh"}. This record does not assert independent reproduction. ### FxTS-Net Accuracy (Original): 88.52. Result ID: 69de183147f34ce924c63c9c. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: Not recorded. This record does not assert independent reproduction. ### LyaNet Accuracy (Original): 88.34. Result ID: 69de183147f34ce924c63c9d. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: Not recorded. This record does not assert independent reproduction. Page 1 of 1. This is an index page, not the full collection.