Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Manifold-Valued Image Generation with Wasserstein Generative Adversarial Nets
Authors: Zhiwu Huang, Jiqing Wu, Luc Van Gool3886-3893
AAAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 16,771 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,763 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On the three datasets, we experimentally demonstrate the proposed manifold-aware WGAN model can generate more plausible manifold-valued images than its competitors. |
| Researcher Affiliation | Academia | Computer Vision Lab, ETH Zurich, Switzerland VISICS, KU Leuven, Belgium |
| Pseudocode | Yes | Algorithm 1 Manifold-aware Wasserstein GAN (manifold WGAN), our proposed algorithm. |
| Open Source Code | No | The paper mentions 'The official code is available at https://github.com/igul222/improved_wgan_training' which refers to an existing WGAN implementation and not the code for the authors' proposed method. |
| Open Datasets | Yes | For the studied manifold-valued image generation problem, we suggest three benchmark evaluations that use the HSV and CB images of the well-known CIFAR-10 (Krizhevsky and Hinton 2009), Image Net (Oord, Kalchbrenner, and Kavukcuoglu 2016), and the popular UCL DT image dataset (Cook et al. 2006). |
| Dataset Splits | Yes | We use the 64 64 version of Image Net, which contains 1,281,149 training images and 49,999 images for testing. |
| Hardware Specification | No | The paper mentions 'We would like to thank Nvidia for donating the GPUs used in this work.' but does not specify any particular GPU models or other hardware details. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, such as programming language versions or library versions. |
| Experiment Setup | Yes | We finally optimize the network using Adam with learning rate 0.0002, decayed linearly to 0 over 100K generator iterations, and batch size 64. |