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..
Domain Generalization with Vital Phase Augmentation
Authors: Ingyun Lee, Wooju Lee, Hyun Myung
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experimental evaluations of our proposed approach, which exhibited improved performance for both clean and corrupted data. VIPAug achieved SOTA performance on the benchmark CIFAR-10 and CIFAR-100 datasets, as well as near-SOTA performance on the Image Net-100 and Image Net datasets. |
| Researcher Affiliation | Academia | Urban Robotics Lab, School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Republic of Korea |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/excitedkid/vipaug. |
| Open Datasets | Yes | We experimentally evaluated the performance of VIPAug on the most widely used CIFAR-10, CIFAR100, Image Net-100, and Image Net datasets. CIFAR-10 and CIFAR-100 comprise 50,000 training images and 10,000 testing images... Image Net consists of 1.2 million images and 1,000 classes. Image Net-100 consists of 100 randomly selected classes of Image Net. |
| Dataset Splits | No | Detailed training setup can be seen in the supplementary material. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | Yes | We trained all methods for 250 epochs. Detailed training setup can be seen in the supplementary material. We used the 2 2 1 argmax filter, and set σvital = 0.001 and σnonvital = 0.014 on CIFAR-10 and σvital = 0.005 and σnonvital = 0.012 on CIFAR-100. |