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.
Generative Modeling through the Semi-dual Formulation of Unbalanced Optimal Transport
Authors: Jaemoo Choi, Jaewoong Choi, Myungjoo Kang
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 25,986 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,560 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 validate these properties empirically through experiments. Moreover, we study the theoretical upper-bound of divergence between distributions in UOT. Our model outperforms existing OT-based generative models, achieving FID scores of 2.97 on CIFAR-10 and 6.36 on Celeb A-HQ-256. |
| Researcher Affiliation | Academia | Jaemoo Choi Seoul National University EMAIL Jaewoong Choi Korea Institute for Advanced Study EMAIL Myungjoo Kang Seoul National University EMAIL |
| Pseudocode | Yes | Algorithm 1 Training algorithm of UOTM |
| Open Source Code | Yes | The code is available at https://github.com/Jae-Moo/UOTM. |
| Open Datasets | Yes | Toy data For all the Toy dataset experiments, we used the same generator and discriminator architectures. ... CIFAR-10 We utilized all 50,000 samples. ... Celeb A-HQ We used all 120,000 samples. |
| Dataset Splits | No | The paper does not explicitly state training, validation, and test splits for the datasets, only the total number of samples used for CIFAR-10 and Celeb A-HQ, and how toy data was composed. |
| Hardware Specification | Yes | Score SDE [69] takes more than 70 hours for training CIFAR-10, 48 hours for DDGAN [80], and 35-40 hours for RGM on four Tesla V100 GPUs. OTM takes approximately 30-35 hours to converge, while our model only takes about 25 hours. |
| Software Dependencies | No | The paper mentions using "Adam optimizer" and states that the implementation for the large model follows Choi et al. [13], but it does not provide specific version numbers for software libraries or frameworks (e.g., PyTorch, TensorFlow, CUDA). |
| Experiment Setup | Yes | For all the Toy dataset experiments, we used the same generator and discriminator architectures. The dimension of the auxiliary variable z is set to one. For a generator, we passed z through two fully connected (FC) layers with a hidden dimension of 128, resulting in 128-dimensional embedding. ... We used a batch size of 256, a learning rate of 10^-4, and 2000 epochs. ... For the small model setting of UOTM, we employed the architecture of Balaji et al. [8]. ... We set a batch size of 128, 200 epochs, a learning rate of 2 x 10^-4 and 10^-4 for the generator and discriminator, respectively. Adam optimizer with β1 = 0.5, β2 = 0.9 is employed. Moreover, we use R1 regularization of λ = 0.2. |