A Kernelised Stein Statistic for Assessing Implicit Generative Models
Authors: Wenkai Xu, Gesine D Reinert
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic distributions and trained generative models on synthetic and real datasets illustrate that the method shows improved power performance compared to existing approaches. |
| Researcher Affiliation | Academia | Wenkai Xu Department of Statistics University of Oxford wenkai.xu@stats.ox.ac.uk, Gesine Reinert Department of Statistics University of Oxford and Alan Turing Institute reinert@stats.ox.ac.uk |
| Pseudocode | Yes | Algorithm 1 Estimating the conditional probability via summary statistics, Algorithm 2 Assessment procedures for implicit generative models |
| Open Source Code | Yes | The code is available at https://github.com/wenkaixl/npksd.git. |
| Open Datasets | Yes | MNIST Dataset This dataset contains 28 28 grey-scale images of handwritten digits [Le Cun et al., 1998]; CIFAR10 Dataset This dataset contains 32 32 RGB coloured images [Krizhevsky, 2009] |
| Dataset Splits | No | The paper mentions training and test samples but does not explicitly provide details about a validation set split or how to create one for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU models, CPU models, or specific cloud instance types. |
| Software Dependencies | No | The paper does not explicitly state specific software dependencies with version numbers, such as libraries, frameworks, or operating systems used for the experiments. |
| Experiment Setup | Yes | For our experiments, 600 samples are generated from the generative models and 100 samples are used for the test; the samples are then down-sampled into 7 7 images, as in Schrab et al. [2021]. The test level is α = 0.05.; For our experiments, 800 samples are generated from the generative models and 200 samples are used for the test; the samples are then down-sampled into 8 8 images, in a similar fashion as in Schrab et al. [2021]. The test level is α = 0.05. |