Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization
Authors: Hao Dong, Ismail Nejjar, Han Sun, Eleni Chatzi, Olga Fink
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that our framework is theoretically well-supported and achieves strong performance in multi-modal DG on the EPIC-Kitchens dataset and the novel Human-Animal-Cartoon (HAC) dataset introduced in this paper. |
| Researcher Affiliation | Academia | 1ETH Zürich 2EPFL |
| Pseudocode | No | The paper describes its methodology in natural language and diagrams, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our source code and HAC dataset are available at https://github.com/donghao51/Sim MMDG. |
| Open Datasets | Yes | We use the EPIC-Kitchens dataset [16] and introduce a novel HAC dataset in this paper, which will be made publicly accessible for further research. ... Our source code and HAC dataset are available at https://github.com/donghao51/Sim MMDG. |
| Dataset Splits | Yes | We train the network for 15 epochs on an RTX 2080 Ti GPU which takes about 20 hours and select the model with the best performance on the validation dataset. |
| Hardware Specification | Yes | Finally, we train the network for 15 epochs on an RTX 2080 Ti GPU which takes about 20 hours and select the model with the best performance on the validation dataset. |
| Software Dependencies | No | The paper mentions using 'MMAction2 toolkit' and 'Adam optimizer' but does not provide specific version numbers for software dependencies like PyTorch, TensorFlow, CUDA, or the MMAction2 toolkit itself. |
| Experiment Setup | Yes | We use the Adam optimizer [39] with a learning rate of 0.0001 and a batch size of 16. The scalar temperature parameter τ is set to 0.1. Additionally, we set αcon = 3.0, αdis = 0.7, and αtrans = 0.1. ... Finally, we train the network for 15 epochs... |