MADG: Margin-based Adversarial Learning for Domain Generalization
Authors: Aveen Dayal, Vimal K B, Linga Reddy Cenkeramaddi, C Mohan, Abhinav Kumar, Vineeth N Balasubramanian
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We extensively experiment with the MADG model on popular real-world DG datasets, VLCS, PACS, Office Home, Domain Net, and Terra Incognita. We evaluate the proposed algorithm on Domain Bed s benchmark and observe consistent performance across all the datasets. |
| Researcher Affiliation | Academia | Aveen Dayal Indian Institute of Technology Hyderabad ai21resch11003@iith.ac.in Vimal K B Indian Institute of Technology Hyderabad vimalkb96@gmail.com Linga Reddy Cenkeramaddi University of Agder linga.cenkeramaddi@uia.no C Krishna Mohan Indian Institute of Technology Hyderabad ckm@cse.iith.ac.in Abhinav Kumar Indian Institute of Technology Hyderabad abhinavkumar@ee.iith.ac.in Vineeth N Balasubramanian Indian Institute of Technology Hyderabad vineethnb@cse.iith.ac.in |
| Pseudocode | Yes | Algorithm 1 Margin-based adversarial learning for Domain Generalization (MADG) |
| Open Source Code | No | No explicit statement or link providing concrete access to the source code for the methodology. |
| Open Datasets | Yes | We perform an extensive evaluation on five benchmark DG datasets for image classification: VLCS [30], PACS [31], Office Home (OH) [32], Terra Incognita (TI) [33] and Domain Net (DN) [34]. |
| Dataset Splits | Yes | We follow [53] in using Test-domain validation procedure for hyperparameter selection. |
| Hardware Specification | No | The paper mentions 'GPU(GB)' in Table 2 for computational cost, but does not provide specific GPU models (e.g., NVIDIA A100), CPU models, or other detailed hardware specifications used for experiments. |
| Software Dependencies | No | The paper mentions using Resnet50 architecture and stochastic gradient descent, but does not provide specific software versions for libraries, frameworks, or languages (e.g., PyTorch version, Python version). |
| Experiment Setup | Yes | All other implementation details including hyperparameters such as learning rate, margin, and weight decay are provided in the Appendix. |