Kernel Mean Matching for Content Addressability of GANs
Authors: Wittawat Jitkrittum, Patsorn Sangkloy, Muhammad Waleed Gondal, Amit Raj, James Hays, Bernhard Schölkopf
ICML 2019 | Conference PDF | Archive PDF | Plain Text | 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 |