Diverse Weight Averaging for Out-of-Distribution Generalization
Authors: Alexandre Rame, Matthieu Kirchmeyer, Thibaud Rahier, Alain Rakotomamonjy, Patrick Gallinari, Matthieu Cord
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, Di WA consistently improves the state of the art on Domain Bed without inference overhead. We now present our evaluation on Domain Bed [12]. By imposing the code, the training procedures and the Res Net50 [52] architecture, Domain Bed is arguably the fairest benchmark for OOD generalization. It includes 5 multi-domain real-world datasets: PACS [51], VLCS [53], Office Home [50], Terra Incognita [54] and Domain Net [55]. |
| Researcher Affiliation | Collaboration | 1Sorbonne Université, CNRS, ISIR, F-75005 Paris, France 2Criteo AI Lab, Paris, France 3Valeo.ai, Paris, France 4Université de Rouen, LITIS, France |
| Pseudocode | Yes | Algorithm 1 Di WA Pseudo-code |
| Open Source Code | Yes | Our code is available at https://github.com/alexrame/diwa. |
| Open Datasets | Yes | We now present our evaluation on Domain Bed [12]. By imposing the code, the training procedures and the Res Net50 [52] architecture, Domain Bed is arguably the fairest benchmark for OOD generalization. It includes 5 multi-domain real-world datasets: PACS [51], VLCS [53], Office Home [50], Terra Incognita [54] and Domain Net [55]. |
| Dataset Splits | Yes | The validation dataset is sampled from S, i.e., we follow Domain Bed s training-domain model selection. |
| Hardware Specification | Yes | Approximately 20000 hours of GPUs (Nvidia V100) on an internal cluster, mostly for the 2640 runs needed in Table 1. |
| Software Dependencies | No | The paper does not provide specific software version numbers for ancillary software dependencies. |
| Experiment Setup | Yes | The experimental setup is further described in Appendix G.1. In our experiments, we thus use the mild search space defined in Table 7, first introduced in SWAD [14]. |