On Relativistic f-Divergences
Authors: Alexia Jolicoeur-Martineau
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | All experiments were done with the spectral GAN architecture for 32x32 images (Miyato et al., 2018) in Pytorch (Paszke et al., 2017). We used the standard hyperparameters: learning rate (lr) = .0002, batch size (k) = 32, and the ADAM optimizer (Kingma & Ba, 2014) with parameters (α1, α2) = (.50, .999). We trained the models for 100k iterations with one critic update per generator update. For the datasets, we used CIFAR-10 (50k training images from 10 categories) (Krizhevsky, 2009), Celeb A (200k of face images from celebrities) (Liu et al., 2015) and CAT (10k images of cats) (Zhang et al., 2008). All models were trained using the same seed (seed=1) with a single GPU. To evaluate the quality of generated outputs, we used the Fr´echet Inception Distance (FID) (Heusel et al., 2017). |
| Researcher Affiliation | Academia | Mila, Universit´e de Montr´eal . |
| Pseudocode | No | The paper contains mathematical formulas and proofs but no structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | See code for details; the code to reproduce the experiments is available on https://github.com/AlexiaJM/relativistic-f-divergences. |
| Open Datasets | Yes | For the datasets, we used CIFAR-10 (50k training images from 10 categories) (Krizhevsky, 2009), Celeb A (200k of face images from celebrities) (Liu et al., 2015) and CAT (10k images of cats) (Zhang et al., 2008). |
| Dataset Splits | No | The paper mentions datasets used but does not explicitly provide specific training/validation/test dataset splits, percentages, or absolute sample counts for each split. |
| Hardware Specification | No | All models were trained using the same seed (seed=1) with a single GPU. The mention of 'single GPU' is not specific enough to determine the hardware specification. |
| Software Dependencies | No | All experiments were done with the spectral GAN architecture for 32x32 images (Miyato et al., 2018) in Pytorch (Paszke et al., 2017). We used the standard hyperparameters: learning rate (lr) = .0002, batch size (k) = 32, and the ADAM optimizer (Kingma & Ba, 2014) with parameters (α1, α2) = (.50, .999). No specific version numbers for PyTorch or other libraries are provided. |
| Experiment Setup | Yes | We used the standard hyperparameters: learning rate (lr) = .0002, batch size (k) = 32, and the ADAM optimizer (Kingma & Ba, 2014) with parameters (α1, α2) = (.50, .999). We trained the models for 100k iterations with one critic update per generator update. |