Introducing Routing Uncertainty in Capsule Networks
Authors: Fabio De Sousa Ribeiro, Georgios Leontidis, Stefanos Kollias
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | we focus on enhancing capsule network properties, and perform a thorough evaluation on pose-aware tasks, observing improvements in performance over previous approaches whilst being more computationally efficient. |
| Researcher Affiliation | Academia | Machine Learning Group University of Lincoln, UK {fdesousaribeiro,skollias}@lincoln.ac.uk Department of Computing Science University of Aberdeen, UK georgios.leontidis@abdn.ac.uk |
| Pseudocode | Yes | Algorithm 1 Capsule Layer with Routing Uncertainty. Returns updated object capsules cj = {aj, Mj} ℓ+ 1, given part capsules ci = {ai, Mi} ℓ. Performs ML/MAP inference of transformation weights W, and variational inference of latent part-object connection variables z. |
| Open Source Code | Yes | 1Code available at: https://github.com/fabio-deep/Routing-Uncertainty-Caps Net |
| Open Datasets | Yes | Small NORB [33] consists of grey-level stereo 96 96 images of 5 objects: each given at 18 different azimuths (0-340), 9 elevations and 6 lighting conditions, with 24,300 training and test set examples. |
| Dataset Splits | Yes | During training we take 32 32 random crops, and centre crops at test time. We train on training set images with azimuths: Atrain = {300, 320, 340, 0, 20, 40}, denoted as familiar viewpoints, and test on test set images containing novel azimuths: Atest = {60, 80, . . . , 280}. Similarly, for the elevation viewpoints we train on Etrain = {30, 35, 40}, and test on Etest = {45, 50, . . . , 70}. |
| Hardware Specification | No | We would like to gratefully acknowledge the support of NVIDIA Corporation with the donation of GPUs used for this research. (No specific GPU model is mentioned, only 'GPUs'.) |
| Software Dependencies | No | Both are readily available in Py Torch and Tensorflow respectively [27, 28]. (Specific version numbers for PyTorch or TensorFlow are not provided.) |
| Experiment Setup | Yes | A single 5 5 Conv layer with f0 filters and stride 2 precedes four capsule layers...In all experiments, we use Adam [30] with default parameters and a batch size of 128 for training. |