Notice: 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. Full accuracy metrics and methodology are described in [1].
Composite Functional Gradient Learning of Generative Adversarial Models
Authors: Rie Johnson, Tong Zhang
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments on image generation show the effectiveness of our new method. We tested x ICFG on the image generation task. |
| Researcher Affiliation | Industry | 1RJ Research Consulting, Tarrytown, NY, USA 2Tencent AI Lab, Shenzhen, China. |
| Pseudocode | Yes | Algorithm 1 CFG: Composite Functional Gradient Learning of GAN; Algorithm 2 ICFG: Incremental CFG; Algorithm 3 x ICFG: Approximate ICFG; Algorithm 4 GAN (Goodfellow et al., 2014) |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that its source code is publicly available. |
| Open Datasets | Yes | We used MNIST, the Street View House Numbers dataset (SVHN) (Netzer et al., 2011), and the large-scale scene understanding (LSUN) dataset. |
| Dataset Splits | Yes | The number of real images used for training was 60K (MNIST), 521K (SVHN), 2.6 million (LSUN BR+LR), and 1.4 million (LSUN T+B). Tuning was done based on the inception score on the validation set of 10K input vectors (i.e., 10K 100-dim Gaussian vectors), and we report inception scores on the test set of 10K input vectors, disjoint from the validation set. |
| Hardware Specification | Yes | All the experiments were done using a single NVIDIA Tesla P100. |
| Software Dependencies | No | The paper mentions optimizers like rmsprop and Adam, but it does not provide specific version numbers for any software, libraries, or frameworks used (e.g., Python, PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | Table 1. Meta-parameters for x ICFG. Input: real data x 1, . . . , x n, initial generator G0(z) with generated data {G0(z1), . . . , G0(zm)}. Meta-parameter: T. ... The scaling function s(x) in ICFG was set to s(x) = 1. ... The prior pz was set to generate 100-dimensional Gaussian vectors with zero mean and standard deviation 1. All the experiments were done using a single NVIDIA Tesla P100. |