Quantifying and Improving Transferability in Domain Generalization
Authors: Guojun Zhang, Han Zhao, Yaoliang Yu, Pascal Poupart
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we evaluate the transferability of the feature embeddings learned by existing algorithms for domain generalization. Experimental results show that the proposed algorithm achieves consistent improvement over many state-of-the-art algorithms, corroborating our theoretical findings. |
| Researcher Affiliation | Academia | Guojun Zhang School of Computer Science University of Waterloo Vector Institute guojun.zhang@uwaterloo.ca Han Zhao Department of Computer Science University of Illinois at Urbana-Champaign hanzhao@illinois.edu Yaoliang Yu School of Computer Science University of Waterloo Vector Institute yaoliang.yu@uwaterloo.ca Pascal Poupart School of Computer Science University of Waterloo Vector Institute ppoupart@uwaterloo.ca |
| Pseudocode | Yes | Algorithm 1: Algorithm for evaluating transferability among multiple domains and Algorithm 2: Transfer algorithm for domain generalization |
| Open Source Code | Yes | Code available at https://github.com/Gordon-Guojun-Zhang/Transferability-NeurIPS2021. |
| Open Datasets | Yes | We test it over various benchmark datasets, including Rotated MNIST, PACS, Office-Home and WILDS-FMo W. [23] Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al. WILDS: A benchmark of in-the-wild distribution shifts. ar Xiv preprint ar Xiv:2012.07421, 2020. |
| Dataset Splits | No | The paper mentions using standard benchmarks and refers to Appendix C for detailed experimental settings, but the main text does not provide specific train/validation/test dataset split percentages or sample counts for reproduction. |
| Hardware Specification | No | The paper mentions 'Vector Institute for providing the GPU cluster' but does not specify exact GPU models, CPU models, memory amounts, or other detailed computer specifications used for running experiments. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with their version numbers (e.g., programming languages, libraries, frameworks) used in the experiments. |
| Experiment Setup | Yes | We take lr = 0.01 for Rotated MNIST and lr = 0.001 for other datasets. We used Adam optimizer with a learning rate of 10 3 for the feature extractor and 10 2 for the classifier. The batch size is 64. We train for 10 epochs. We use ReLU as activation function. |