Sinkhorn Natural Gradient for Generative Models
Authors: Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we quantitatively compare Si NG with state-of-the-art SGD-type solvers on generative tasks to demonstrate its efficiency and efficacy of our method. |
| Researcher Affiliation | Academia | Department of Electrical and Systems Engineering :Department of Mathematics University of Pennsylvania {zebang@seas,zwang423@math,aribeiro@seas,hassani@seas}.upenn.edu |
| Pseudocode | No | The paper describes algorithms conceptually and refers to their implementation, but it does not include any explicitly labeled pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper refers to an Appendix E for implementation details but does not provide an explicit statement about open-sourcing the code or a link to a code repository. |
| Open Datasets | Yes | In our experiments, we pretrain the discriminators for the celeb A and cifar10 datasets. [...] Our experiment considers a specific instance of problem (30) where we take the measure β to be the distribution of the images in the Celeb A dataset. [...] Two specific instances are considered: we take the measure β to be the distribution of the images in either the Celeb A or the Cifar10 dataset. |
| Dataset Splits | No | The paper mentions using Celeb A and Cifar10 datasets but does not specify the training, validation, or test splits (e.g., percentages, sample counts, or references to standard splits with citations) within the provided text. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, or cloud resources) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'modern auto-differential mechanisms such as Py Torch' and 'ADAM as the optimizer' but does not specify version numbers for any software dependencies, which are necessary for reproducibility. |
| Experiment Setup | Yes | The entropy regularization parameter γ is set to 0.01 for both the objective and the constraint. [...] The entropy regularization of the Sinkhorn divergence objective is set to γ 100 as suggested in Table 2 of [Genevay et al., 2018]. The regularization for the constraint is set to γ 1 in Si NG. We used ADAM as the optimizer for the discriminators (with step size 10 3 and batch size 4000). |