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..
Influence Estimation for Generative Adversarial Networks
Authors: Naoyuki Terashita, Hiroki Ohashi, Yuichi Nonaka, Takashi Kanemaru
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally verified that our influence estimation method correctly inferred the changes in GAN evaluation metrics. We also demonstrated that the removal of the identified harmful instances effectively improved the model s generative performance with respect to various GAN evaluation metrics. |
| Researcher Affiliation | Industry | Naoyuki Terashita Hiroki Ohashi Yuichi Nonaka Takashi Kanemaru Hitachi, Ltd. Tokyo, Japan |
| Pseudocode | Yes | Algorithm 1 Training Phase and Algorithm 2 Inference Phase |
| Open Source Code | Yes | Code is at https://github.com/hitachi-rd-cv/influence-estimation-for-gans |
| Open Datasets | Yes | DCGAN (Radford et al., 2016) trained with MNIST (Le Cun et al., 1998) |
| Dataset Splits | No | The paper mentions training and test datasets but does not explicitly provide details about a validation dataset split, its size, or how it was used for hyperparameter tuning during training. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types) used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | Table 2: Hyper parameters in Section 5.1. and Table 5: Hyper parameters in Section 5.2. |