Influence Estimation for Generative Adversarial Networks
Authors: Naoyuki Terashita, Hiroki Ohashi, Yuichi Nonaka, Takashi Kanemaru
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | 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. |