Mixup-Induced Domain Extrapolation for Domain Generalization
Authors: Meng Cao, Songcan Chen
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, EDM has been plugged into several methods in both closed and open set settings, achieving up to 5.73% improvement. Extensively experiments are constructed to comprehensively evaluate the effectiveness of EDM on two datasets both in closed and open set settings. |
| Researcher Affiliation | Academia | Meng Cao1,2, Songcan Chen1,2* 1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics 2MIIT Key Laboratory of Pattern Analysis and Machine Intelligence {meng.cao, s.chen}@nuaa.edu.cn |
| Pseudocode | Yes | Detailed Algorithm is provided in Appendix. |
| Open Source Code | Yes | The code is available at https://github.com/Alrash/EDM. |
| Open Datasets | Yes | For the architecture, we use Res Net-18 as backbone on three datasets, i.e., PACS, Office-Home, and Domain Net datasets. |
| Dataset Splits | Yes | For both settings, we follow corresponding settings from the previous methods, i.e., the same closed-set setting as (Lu et al. 2022), and the same open-set setting as (Shu et al. 2021). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | No | The paper states 'For both settings, we follow corresponding settings from the previous methods,' but does not provide specific hyperparameter values or detailed training configurations within the main text. |