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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |