Learning Invariant Representations with a Nonparametric Nadaraya-Watson Head
Authors: Alan Wang, Minh Nguyen, Mert Sabuncu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our approach on three challenging real-world domain generalization tasks in computer vision. and 5 Experiments and Results and Table 2: Metric average standard deviation for all datasets (%). Higher is better. Bold is best and underline is second-best. |
| Researcher Affiliation | Academia | Alan Q. Wang Cornell University Minh Nguyen Cornell University Mert R. Sabuncu Cornell University |
| Pseudocode | No | The paper describes the proposed approach and optimization details in text, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/alanqrwang/nwhead. |
| Open Datasets | Yes | We experiment on 3 real-world domain generalization tasks. Two are from the WILDS benchmark [23], and the third is a challenging melanoma detection task. Details on the datasets are summarized in Table 1, and further information is provided in the Appendix. and 1. The Camelyon-17 dataset [4]... 2. The melanoma dataset is from the International Skin Imaging Collaboration (ISIC) archive5... 3. The Functional Map of the World (FMo W) dataset [6]... |
| Dataset Splits | Yes | For WILDS datasets, we follow all hyperparameters, use model selection techniques, and report metrics as specified by the benchmark. ... Similarly for ISIC, we use a pretrained Res Net-50 backbone as φ with no augmentations, and perform model selection on an OOD validation set. and The training dataset is drawn from the first 3 hospitals, while out-of-distribution validation and out-of-distribution test datasets are sampled from the 4th hospital and 5th hospital, respectively. |
| Hardware Specification | Yes | All training and inference is done on an Nvidia A6000 GPU and all code is written in Pytorch.6. |
| Software Dependencies | No | The paper mentions 'Pytorch' as the software used, but does not specify a version number for it or any other software dependencies. |
| Experiment Setup | Yes | Table 3: Hyperparameter settings for various datasets. and For each model variant, we train 5 separate models with different random seeds, and perform model selection on an out-of-distribution (OOD) validation set. |