Unsupervised Object Segmentation by Redrawing
Authors: Mickaël Chen, Thierry Artières, Ludovic Denoyer
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experiment with this method on different datasets and demonstrate the good quality of extracted masks. (...) Finally, we present experimental results on three datasets in Section 5 that explore the feasibility of unsupervised segmentation within our framework and compare its performance against a baseline supervised with few labeled examples. |
| Researcher Affiliation | Collaboration | Mickaël Chen Sorbonne Université, CNRS, LIP6, F-75005, Paris, France mickael.chen@lip6.fr Thierry Artières Aix Marseille Univ, Université de Toulon, CNRS, LIS, Marseille, France Ecole Centrale Marseille thierry.artieres@centrale-marseille.fr Ludovic Denoyer Facebook Artificial Intelligence Research denoyer@fb.com |
| Pseudocode | Yes | The final learning algorithm follows classical GAN schema [3, 17, 39, 57] by updating the generator and the discriminator alternatively with the update functions presented in Algorithm 1. (...) Algorithm 1 Networks update functions |
| Open Source Code | Yes | The code, dataset splits and pre-trained models are also available open-source 1. 1https://github.com/mickael Chen/Re DO |
| Open Datasets | Yes | Flowers dataset [40, 41] is composed of 8189 images of flowers. (...) Labeled Faces in the Wild dataset [25, 33] is a dataset of 13233 faces. (...) The Caltech-UCSD Birds 200 2011 (CUB-200-2011) dataset [53] is a dataset containing 11788 photographs of birds. (...) colored MNIST [34] numbers: |
| Dataset Splits | Yes | Flowers dataset [40, 41] (...) We split into sets of 6149 training images, 1020 validation and 1020 test images (...) Labeled Faces in the Wild dataset [25, 33] (...) The test set is composed of 1600 images, and the validation set of 1327 images. (...) The Caltech-UCSD Birds 200 2011 (CUB-200-2011) dataset [53] (...) We use 10000 images for our training split, 1000 for the test split, and the rest for validation. |
| Hardware Specification | Yes | We used mini-batches of size 25 and ran each experiment on a single NVidia Tesla P100 GPU. |
| Software Dependencies | No | The paper mentions using ADAM optimizer and inspirations from PSPNet and SAGAN architectures, but does not provide specific version numbers for software dependencies or libraries like Python, PyTorch, or TensorFlow. |
| Experiment Setup | Yes | Learning rates are set to 10 4 except for the mask network F which uses a smaller value of 10 5. We sample noise vectors zi of size 32 (except for MNIST where we used vectors of size 16) from N(0, Id) distribution. We used mini-batches of size 25 and ran each experiment on a single NVidia Tesla P100 GPU. |