Moreau-Yosida $f$-divergences
Authors: Dávid Terjék
ICML 2021 | Conference PDF | Archive PDF | Plain Text | 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). |