Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Moreau-Yosida $f$-divergences
Authors: Dávid Terjék
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | As an application of our results, we propose the Moreau Yosida f-GAN, providing an implementation of the variational formulas for the Kullback-Leibler, reverse Kullback-Leibler, χ2, reverse χ2, squared Hellinger, Jensen-Shannon, Jeffreys, triangular discrimination and total variation divergences as GANs trained on CIFAR-10, leading to competitive results and a simple solution to the problem of uniqueness of the optimal critic. |
| Researcher Affiliation | Academia | Alfréd Rényi Institute of Mathematics, Budapest, Hungary. |
| Pseudocode | Yes | Algorithm 1 Calculate γφ,ν(f) and fγφ,ν(f) |
| Open Source Code | Yes | Source code to reproduce the experiments is available at https://github.com/renyi-ai/moreau-yosida-f-divergences. |
| Open Datasets | Yes | As an application of our results, we propose the Moreau Yosida f-GAN, providing an implementation of the variational formulas for the Kullback-Leibler, reverse Kullback-Leibler, χ2, reverse χ2, squared Hellinger, Jensen-Shannon, Jeffreys, triangular discrimination and total variation divergences as GANs trained on CIFAR-10, leading to competitive results and a simple solution to the problem of uniqueness of the optimal critic. |
| Dataset Splits | No | The paper mentions training on CIFAR-10 and reports results (IS, FID) which typically involve a test set. However, it does not explicitly specify the training, validation, or test split percentages or methodology beyond stating 'minibatches' were used for training. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The implementation was done in Tensor Flow. However, no specific version number for TensorFlow or any other software dependency is provided. |
| Experiment Setup | Yes | Training was done for 100000 iterations, with 5 gradient descent step per iteration for the critic, and 1 for the generator. ... This particular experiment used ℓ= 10 and φ corresponding to the Kullback-Leibler divergence, but we observed identical behavior in other hyperparameter settings as well with a range of α close to 1. ... The implementation was done in Tensor Flow, using the residual critic and generator architectures from Gulrajani et al. (2017). |