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.
A Variational Inequality Perspective on Generative Adversarial Networks
Authors: Gauthier Gidel, Hugo Berard, Gaëtan Vignoud, Pascal Vincent, Simon Lacoste-Julien
ICLR 2019 | Venue PDF | LLM Run Details | Input Tokens: 40,329 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,768 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 | Our goal in this experimental section is not to provide new state-of-the art results with architectural improvements or a new GAN formulation, but to show that using the techniques (with theoretical guarantees in the monotone case) that we introduced earlier allows us to optimize standard GANs in a better way. |
| Researcher Affiliation | Collaboration | 1Mila & DIRO, University of Montreal 2Canada CIFAR AI Chair 3Facebook Artificial Intelligence Research |
| Pseudocode | Yes | Algorithm 1 Avg SGD; Algorithm 2 Avg Extra SGD; Algorithm 3 Avg Past Extra SGD; Algorithm 4 Extra-Adam; Algorithm 5 Re-used mini-batches for stochastic extrapolation (Re Extra SGD) |
| Open Source Code | Yes | Code available at https://gauthiergidel.github.io/projects/vip-gan.html. |
| Open Datasets | Yes | We evaluate the proposed techniques in the context of GAN training, which is a challenging stochastic optimization problem where the objectives of both players are non-convex. We propose to evaluate the Adam variants of the different optimization algorithms (see Alg. 4 for Adam with extrapolation) by training two different architectures on the CIFAR10 dataset (Krizhevsky and Hinton, 2009). |
| Dataset Splits | Yes | We evaluate the proposed techniques in the context of GAN training, which is a challenging stochastic optimization problem where the objectives of both players are non-convex. We propose to evaluate the Adam variants of the different optimization algorithms (see Alg. 4 for Adam with extrapolation) by training two different architectures on the CIFAR10 dataset (Krizhevsky and Hinton, 2009). |
| Hardware Specification | Yes | all experiments were run on a NVIDIA Quadro GP100 GPU |
| Software Dependencies | No | The paper mentions using Adam and SGD optimizers but does not provide specific version numbers for any software libraries or frameworks (e.g., PyTorch, TensorFlow, Python, CUDA). |
| Experiment Setup | Yes | For each algorithm, we did an extensive search over the hyperparameters of Adam. We fixed β1 = 0.5 and β2 = 0.9 for all methods as they seemed to perform well. We note that as proposed by Heusel et al. (2017), it is quite important to set different learning rates for the generator and discriminator. Experiments were run with 5 random seeds for 500,000 updates of the generator. [...] (DCGAN) WGAN Hyperparameters Batch size = 64 Number of generator update = 500, 000 Adam β1 = 0.5 Adam β2 = 0.9 Weight clipping for the discriminator = 0.01 Learning rate for generator = 2 10 5 (for Adam1, Adam5, Past Extra Adam, Optimistic Adam) = 5 10 5 (for Extra Adam) Learning rate for discriminator = 2 10 4 (for Adam1, Adam5, Past Extra Adam, Optimistic Adam) = 5 10 4 (for Extra Adam) β for EMA = 0.999 |