Controlling generative models with continuous factors of variations
Authors: Antoine Plumerault, Hervé Le Borgne, Céline Hudelot
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | 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. |