Domain Generalization by Learning and Removing Domain-specific Features
Authors: Yu Ding, Lei Wang, Bin Liang, Shuming Liang, Yang Wang, Fang Chen
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our framework achieves superior performance compared with state-of-the-art methods. |
| Researcher Affiliation | Academia | Yu Ding University of Wollongong yd624@uowmail.edu.au Lei Wang University of Wollongong leiw@uow.edu.au Bin Liang University of Technology Sydney Bin.Liang@uts.edu.au Shuming Liang University of Technology Sydney Shuming.Liang@uts.edu.au Yang Wang University of Technology Sydney Yang.Wang@uts.edu.au Fang Chen University of Technology Sydney Fang.Chen@uts.edu.au |
| Pseudocode | No | The paper does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/yulearningg/LRDG. |
| Open Datasets | Yes | We evaluate our framework on three object recognition datasets for domain generalization. PACS [27]... VLCS [39]... Office-Home [40]... |
| Dataset Splits | No | The source datasets are split into a training set and a validation set. The learning rate is decided by the validation set. However, the paper does not specify the exact percentages or counts for these splits in the main text. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, memory) used for running its experiments in the main text. |
| Software Dependencies | No | The paper mentions software components like 'Alex Net', 'Res Net18', 'Res Net50', 'U-net', 'Stochastic Gradient Descent', 'cross-entropy loss', 'entropy loss', and 'pixel-wise l2 loss' but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | We set λ1 = 1 for all the experiments. We give equal weight to the classification loss and the uncertainty loss for training the domain-specific classifiers. For λ2 and λ3, we follow the literature [13, 4] and directly use the leave-one-domain-out cross-validation to select their values. ... Alex Net and Res Net are pre-trained by Image Net [37] for all the experiments. |