Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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 EMAIL Vimal K B Indian Institute of Technology Hyderabad EMAIL Linga Reddy Cenkeramaddi University of Agder EMAIL C Krishna Mohan Indian Institute of Technology Hyderabad EMAIL Abhinav Kumar Indian Institute of Technology Hyderabad EMAIL Vineeth N Balasubramanian Indian Institute of Technology Hyderabad EMAIL |
| 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. |