G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object Detection
Authors: Fan Wu, Jinling Gao, Lanqing Hong, Xinbing Wang, Chenghu Zhou, Nanyang Ye
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the S-DGOD urban-scene datasets demonstrate that the proposed G-NAS achieves SOTA performance compared to baseline methods. ... Experiments Experimental Setup Datasets. ... Ablation Study |
| Researcher Affiliation | Collaboration | 1 Shanghai Jiao Tong University, Shanghai, China 2 Huawei Noah s Ark Lab, Hong Kong, China |
| Pseudocode | Yes | Algorithm 1: G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object Detection |
| Open Source Code | Yes | Codes are available at https://github.com/wufan-cse/G-NAS. |
| Open Datasets | Yes | To evaluate different methods single-domain generalization ability, we follow the setting proposed by Wu and Deng (2022). The dataset contains five urban-scene domains with distinct weather conditions, including Daytime-Sunny, Daytime-Foggy, Dusk-Rainy, Night-Sunny, and Night-Rainy. |
| Dataset Splits | No | Daytime-Sunny is the source training domain and the other four domains are only used for testing. ... In this paper, we use Ltrain to optimize α as the in-domain (i.d.) validation set is not suitable for S-DGOD as we aim to improve Oo D generalization ability instead of selecting models with optimal i.d. performance. |
| Hardware Specification | No | The paper only states 'All experiments are conducted on a computer with 8 GPUs.' without specifying the make, model, or type of GPUs or other hardware components. |
| Software Dependencies | No | The paper describes the optimizer (SGD) and training parameters but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, or specific library versions). |
| Experiment Setup | Yes | We train all models until full convergence for 12 epochs. We set the λg to 1.0. All parameters in our NAS framework are randomly initialized. We apply an SGD optimizer with the learning rate set to 0.02 and we set the batch size to 4 per GPU. |