Domain Generalization with Vital Phase Augmentation
Authors: Ingyun Lee, Wooju Lee, Hyun Myung
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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. |