MetaReg: Towards Domain Generalization using Meta-Regularization
Authors: Yogesh Balaji, Swami Sankaranarayanan, Rama Chellappa
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental validations on computer vision and natural language datasets indicate that our method can learn regularizers that achieve good cross-domain generalization. In this section, we describe the experimental validation of our proposed approach. We perform experiments on two benchmark domain generalization datasets Multi-domain image recognition using PACS dataset [18] and sentiment classification using Amazon Reviews dataset [2]. |
| Researcher Affiliation | Collaboration | Yogesh Balaji Department of Computer Science University of Maryland College Park, MD yogesh@cs.umd.edu Swami Sankaranarayanan Butterfly Network Inc. New York, NY swamiviv@butterflynetinc.com Rama Chellappa Department of Electrical and Computer Engineering University of Maryland College Park, MD rama@umiacs.umd.edu |
| Pseudocode | Yes | The entire algorithm is given in Algorithm 1 |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | We perform experiments on two benchmark domain generalization datasets Multi-domain image recognition using PACS dataset [18] and sentiment classification using Amazon Reviews dataset [2]. |
| Dataset Splits | No | The paper mentions 'meta-train set' and 'meta-test set' for training the regularizer, but these are not traditional validation splits for hyperparameter tuning of the main model. It does not provide specific percentages or counts for a validation set. |
| Hardware Specification | No | The paper does not specify any particular hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions models like Alexnet and Resnet but does not specify any software versions for libraries, frameworks, or programming languages (e.g., PyTorch version, Python version). |
| Experiment Setup | Yes | All our models are trained using the SGD optimizer with learning rate 5e 4 and a batch size of 64. All models were trained using SGD optimizer with a learning rate of 0.001 and momentum 0.9. The hyper-parameters α1 and α2 are both set as 0.001. All models were trained using an SGD optimizer with learning rate 0.01 and momentum 0.9 for 5000 iterations. |