Approximability of Discriminators Implies Diversity in GANs
Authors: Yu Bai, Tengyu Ma, Andrej Risteski
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our preliminary experiments show that on synthetic datasets the test IPM is well correlated with KL divergence or the Wasserstein distance, indicating that the lack of diversity in GANs may be caused by the sub-optimality in optimization instead of statistical inefficiency. |
| Researcher Affiliation | Academia | Yu Bai Stanford University yub@stanford.edu Tengyu Ma Stanford University tengyuma@stanford.edu Andrej Risteski MIT risteski@mit.edu |
| Pseudocode | Yes | Algorithm 1 Discriminator family with restricted approximability for degenerate manifold |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We design synthetic datasets, set up suitable generators, and train GANs with either our theoretically proposed discriminator class with restricted approximability, or vanilla neural network discriminators of reasonable capacity. [...] We set the ground truth distribution to be a unit circle or a Swiss roll curve, sampled from Circle : (x, y) Uniform( (x, y) : x2 + y2 = 1 ) Swiss roll : (x, y) = (z cos(4πz), z sin(4πz)) : z Uniform([0.25, 1]). |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or detailed methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'RMSProp optimizer (Tieleman & Hinton, 2012)' and the 'POT package' but does not specify version numbers for these or other software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | The generator architecture is 2-50-50-2, and the discriminator architecture is 2-50-50-1. We use the RMSProp optimizer (Tieleman & Hinton, 2012) as our update rule, the learning rates are 10 4 for both the generator and discriminator, and we perform 10 steps on the discriminator in between each generator step. |