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..
Controlling generative models with continuous factors of variations
Authors: Antoine Plumerault, Hervé Le Borgne, Céline Hudelot
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our method qualitatively and quantitatively, both for GANs and variational auto-encoders. and 3 EXPERIMENTS Datasets: We performed experiments on two datasets. |
| Researcher Affiliation | Academia | Antoine Plumerault , Hervé Le Borgne , Céline Hudelot CEA, LIST, Laboratoire Analyse Sémantique Texte et Image, Gif-sur-Yvette, F-91191 France Université Paris-Saclay, Centrale Supélec, 91190, Gif-sur-Yvette, France. |
| Pseudocode | Yes | Algorithm 1: Create a dataset of trajectories in the latent space which corresponds to a transformation T in the pixel space. |
| Open Source Code | Yes | All our experiments have been implemented with Tensor Flow 2.0 (Abadi et al., 2015) and the corresponding code is available on github here. |
| Open Datasets | Yes | Datasets: We performed experiments on two datasets. The first one is d Sprites (Matthey et al., 2017), composed of 737280 binary 64 64 images containing a white shape on a dark background. The second dataset is ILSVRC (Russakovsky et al., 2015), containing 1.2M natural images from one thousand different categories. |
| Dataset Splits | No | The paper refers to |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | Yes | All our experiments have been implemented with Tensor Flow 2.0 (Abadi et al., 2015) |
| Experiment Setup | Yes | The models were trained on d Sprites (Matthey et al., 2017) with an Adam optimizer during 1e5 steps with a batch size of 128 images and a learning rate of 5e 4. |