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 [1].
ExGAN: Adversarial Generation of Extreme Samples
Authors: Siddharth Bhatia, Arjit Jain, Bryan Hooi6750-6758
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real US precipitation data show that our method generates realistic samples, based on visual inspection and quantitative measures, in an ef๏ฌcient manner. |
| Researcher Affiliation | Academia | 1National University of Singapore 2IIT Bombay EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Distribution Shifting; Algorithm 2: EVT-based Conditional Generation |
| Open Source Code | Yes | Reproducibility: Our code and datasets are publicly available at https://github.com/Stream-AD/Ex GAN. |
| Open Datasets | Yes | Reproducibility: Our code and datasets are publicly available at https://github.com/Stream-AD/Ex GAN. |
| Dataset Splits | Yes | We use the daily spatial rainfall distribution for the duration January 2010 to December 2016 as our training set, and for the duration of January 2017 to August 2020 as our test set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | Yes | Our Ex GAN model is implemented in Python 3.6 using PyTorch 1.4.0. |
| Experiment Setup | Yes | Details about our experimental setup, network architecture and software implementation can be found in Appendix A, B and C respectively. |