Discretization Drift in Two-Player Games
Authors: Mihaela C Rosca, Yan Wu, Benoit Dherin, David Barrett
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To understand the effect of DD on more complex adversarial games, we analyze GANs trained for image generation on the CIFAR10 dataset. We follow the model architectures from Spectral-Normalized GANs (Miyato et al., 2018). Both players have millions of parameters. We employ the original GAN formulation, where the discriminator is a binary classifier trained to classify data from the dataset as real and model samples as fake; the generator tries to generate samples that the discriminator considers as real. This can be formulated as a zero-sum game: E = Ep (x) log Dφ(x) + Epθ(z) log(1 Dφ(Gθ(z)) |
| Researcher Affiliation | Collaboration | Mihaela Rosca 1 2 Yan Wu 1 Benoit Dherin 3 David G.T. Barrett 1 1Deep Mind, London, UK 2Center for Artificial Intelligence, University College London 3Google, Dublin, Ireland. |
| Pseudocode | No | No pseudocode or algorithm blocks are explicitly presented in the paper. The paper contains mathematical equations and derivations but no structured algorithm formats. |
| Open Source Code | Yes | The code associated with this work can be found at https://github.com/deepmind/deepmind-research/dd_two_player_games. |
| Open Datasets | Yes | To understand the effect of DD on more complex adversarial games, we analyze GANs trained for image generation on the CIFAR10 dataset. |
| Dataset Splits | No | The paper mentions using the CIFAR10 dataset but does not explicitly provide details about the training, validation, and test splits (e.g., percentages or sample counts) within the main text. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions optimizers like SGD and Adam, but it does not list any specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For SGD we use learning rates {5 10 2, 1 10 2, 5 10 3, 1 10 3} for each player; for Adam, we use learning rates {1 10 4, 2 10 4, 3 10 4, 4 10 4}, which have been widely used in the literature (Miyato et al., 2018). When comparing to Runge-Kutta (RK4) to assess the effect of DD we always use the same learning rates for both players. |