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..
Flat Metric Minimization with Applications in Generative Modeling
Authors: Thomas Möllenhoff, Daniel Cremers
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, we show that the proposed shift to k > 0 leads to interpretable and disentangled latent representations which behave equivariantly to the specified oriented tangent planes. |
| Researcher Affiliation | Academia | 1Department of Informatics, Technical University of Munich, Garching, Germany. |
| Pseudocode | No | The paper describes mathematical formulations and implementation details, but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | See https://github.com/moellenh/flatgan for a PyTorch implementation to reproduce Fig. 6 and Fig. 7. |
| Open Datasets | Yes | For MNIST, we compute the tangent vectors manually by rotation and dilation of the digits, similar as done by Simard et al. (1992; 1998). For the small NORB example, the tangent vectors are given as differences between the corresponding images. As observed in the figures, the proposed formulation leads to interpretable latent codes. |
| Dataset Splits | No | The paper mentions using datasets like MNIST, small NORB, and tinyvideos but does not provide specific details on training, validation, or test splits (e.g., percentages, sample counts, or explicit cross-validation setup). |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU/GPU models, memory, or cloud instance types used for running experiments. |
| Software Dependencies | No | The paper mentions 'PyTorch implementation' but does not specify version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The specific hyperparameters, architectures and tangent vector setups used in practice3 are detailed in Appendix B. |