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.
Discriminator optimal transport
Authors: Akinori Tanaka
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 15,761 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,914 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 | Based on some experiments and a bit of OT theory, we propose discriminator optimal transport (DOT) scheme to improve generated images. We show that it improves inception score and FID calculated by unconditional GAN trained by CIFAR-10, STL-10 and a public pre-trained model of conditional GAN trained by Image Net. |
| Researcher Affiliation | Academia | Akinori Tanaka Mathematical Science Team, RIKEN Center for Advanced Intelligence Project (AIP) 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan Interdisciplinary Theoretical and Mathematical Sciences Program (i THEMS), RIKEN 2-1 Hirosawa, Wako, Saitama 351-0198, Japan Department of Mathematics, Faculty of Science and Technology, Keio University 3-14-1 Hiyoshi, Kouhoku-ku, Yokohama 223-8522, Japan EMAIL |
| Pseudocode | Yes | Algorithm 1 Target space optimal transport by gradient descent, Algorithm 2 Latent space optimal transport by gradient descent, Algorithm 3 Latent space conditional optimal transport by gradient descent |
| Open Source Code | Yes | One can download our codes from https://github.com/AkinoriTanaka-phys/DOT. |
| Open Datasets | Yes | trained by CIFAR-10, STL-10 and a public pre-trained model of conditional GAN trained by Image Net. [...] To calculate FID, we use available 798,900 image files in ILSVRC2012 dataset. |
| Dataset Splits | No | No specific dataset split information (percentages, sample counts, or explicit methodology for splitting) found for training, validation, or test sets. The paper mentions using standard datasets like CIFAR-10 and ImageNet, which typically have predefined splits, but it does not explicitly state the splits used in this paper. |
| Hardware Specification | No | The paper mentions support from 'RIKEN AIP deep learning environment (RAIDEN) and RIKEN i THEMS' but does not specify any particular hardware components like GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., 'PyTorch 1.9', 'Python 3.8'). While it mentions optimizers like 'Adam' and 'SGD', these are algorithms, not specific software versions. |
| Experiment Setup | Yes | We apply gradient descent updates with with Adam(α, β1, β2) = (0.01, 0, 0.9). [...] ϵ = 0.01 SGD is applied 20 times for CIDAR-10 and 10 times for STL-10. keff is calculated by 100 samples and δ = 0.001. |