Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Mixup-Induced Domain Extrapolation for Domain Generalization
Authors: Meng Cao, Songcan Chen
AAAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 17,808 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,937 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL |
| 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. |