On the Limitations of First-Order Approximation in GAN Dynamics
Authors: Jerry Li, Aleksander Madry, John Peebles, Ludwig Schmidt
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We first show experimentally that standard gradient dynamics of the GMM-GAN often fail to converge due to mode collapse or oscillatory behavior. ... In contrast, we then show that GAN dynamics with an optimal discriminator do converge, both experimentally and provably. Section 5 is titled "Experiments". |
| Researcher Affiliation | Academia | Jerry Li 1 Aleksander M adry 1 John Peebles 1 Ludwig Schmidt 1 1MIT. Correspondence to: Jerry Li <jerryzli@mit.edu>. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper uses a synthetic model (GMM-GAN) and does not specify or provide access information for any publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We set µ = ( 0.5, 0.5) as in Figure 1. ... ran the first order dynamics for 3000 iterations, with constant stepsize 0.3. We then repeated this 120 times for each grid point... |