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..
Kernel Mean Matching for Content Addressability of GANs
Authors: Wittawat Jitkrittum, Patsorn Sangkloy, Muhammad Waleed Gondal, Amit Raj, James Hays, Bernhard Schölkopf
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on various high-dimensional image generation problems (Celeb A-HQ, LSUN bedroom, bridge, tower) show that our approach is able to generate images which are consistent with the input set, while retaining the image quality of the original model. |
| Researcher Affiliation | Academia | 1Empirical Inference Department, Max Planck Institute for Intelligent Systems, Germany 2School of Interactive Computing, Georgia Institute of Technology, USA. |
| Pseudocode | No | The paper describes procedures and optimization problems but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Python code is available at https://github.com/wittawatj/cadgan. |
| Open Datasets | Yes | We consider three categories of the LSUN dataset (Yu et al., 2015): bedroom, bridge, tower, and use pretrained GAN models from Mescheder et al. (2018) which were trained separately on training samples from each category. To show the importance of a nonlinear kernel k in (6), we consider a DCGAN (Radford et al., 2015) model trained on MNIST. |
| Dataset Splits | No | The paper mentions using well-known datasets like MNIST and LSUN but does not explicitly provide specific percentages, counts, or references to predefined train/validation/test splits within the text for reproducibility of data partitioning. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used (e.g., GPU/CPU models, memory amounts) for running its experiments. |
| Software Dependencies | No | The paper mentions 'Pytorch code' and links to GitHub repositories for DCGAN and CNN classifier implementations (e.g., 'https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/dcgan/dcgan.py'), implying PyTorch is used, but it does not specify concrete version numbers for any software dependencies. |
| Experiment Setup | Yes | We compare two different kernels (k in (6)): 1) linear kernel, and 2) the IMQ kernel with kernel parameter c set to 10. For content-based generation, we use the IMQ kernel with parameter c = 100 and set the extractor E to be the output of the layer before the last fully connected layer of a pretrained Places365-Res Net classification model (Zhou et al., 2017). To solve (5), we use Adam (Kingma and Ba, 2015) which relies on the gradient |