Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Flow-based Image-to-Image Translation with Feature Disentanglement
Authors: Ruho Kondo, Keisuke Kawano, Satoshi Koide, Takuro Kutsuna
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 16,890 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,891 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compared our model to the official implementations of VUNet [1] and PUNet [2]. Datasets We employed the celebrity face attributes dataset (Celeb A [19], which consists of images with 40 attribute annotations) as well as our orginal dataset, Cahn Hilliard Cook (CHC) dataset. Table 1: Comparison of FID, LPIPS and c LPIPS. The means and standard deviations of five trials are shown for FID. |
| Researcher Affiliation | Industry | Ruho Kondo Toyota Central R&D Labs. EMAIL Keisuke Kawano Toyota Central R&D Labs. EMAIL Satoshi Koide Toyota Central R&D Labs. EMAIL Takuro Kutsuna Toyota Central R&D Labs. EMAIL |
| Pseudocode | Yes | Algorithm 1 Procedures used to obtain condition-specific diverse images xsp, condition-invariant diverse images xiv, and style transformed images xtrans. ( )mean is the mean of ( ). |
| Open Source Code | No | The paper does not contain an explicit statement or link providing access to the open-source code for the described methodology. |
| Open Datasets | Yes | Datasets We employed the celebrity face attributes dataset (Celeb A [19], which consists of images with 40 attribute annotations) as well as our orginal dataset, Cahn Hilliard Cook (CHC) dataset. |
| Dataset Splits | Yes | The number of data for training, validation and testing in Celeb A were 162,770, 1,9867 and 19,962, respectively, which follows an official train/val/test partitions. |
| Hardware Specification | Yes | All experiments were carried out on a single NVIDIA Tesla P100 GPU. |
| Software Dependencies | Yes | We implemented our model with Tensor Flow version 1.10.0 [30]. |
| Experiment Setup | Yes | In all of our experiments, α = 0.01 and β = 0.1 were used. For training, we used the default settings of the Adam [32] optimizer with a learning rate of 10 4 and batch size of 32. |