NaturalInversion: Data-Free Image Synthesis Improving Real-World Consistency
Authors: Yujin Kim, Dogyun Park, Dohee Kim, Suhyun Kim1201-1209
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the performance of Natural Inversion on CIFAR-10 and CIFAR-100 (Krizhevsky and Hinton 2009). Our experiments contain two parts, (1) analysis of our method: we verify that our method can synthesize more natural images (2) applications: we ensure the effectiveness of Natural Inversion on various applications in data-free conditions. |
| Researcher Affiliation | Academia | Yujin Kim1,2, Dogyun Park1,2, Dohee Kim2, Suhyun Kim2* 1Korea University, Republic of Korea 2Korea Institute of Science and Technology, Republic of Korea |
| Pseudocode | Yes | Algorithm 1: Natural Inversion Algorithm |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the described methodology. |
| Open Datasets | Yes | We evaluate the performance of Natural Inversion on CIFAR-10 and CIFAR-100 (Krizhevsky and Hinton 2009). |
| Dataset Splits | No | The paper mentions 'Our experiments settings can be found in appendix' but does not provide specific details on validation splits within the main text. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or cloud computing resources used for the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | We set the mini-batch size as 128 and train the model for 200 epochs using an SGD optimizer with a 0.05 learning rate. |