Group Equivariant Capsule Networks
Authors: Jan Eric Lenssen, Matthias Fey, Pascal Libuschewski
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Last, we apply this combined architecture as proof of concept application of our framework to MNIST datasets and verify the properties experimentally. |
| Researcher Affiliation | Academia | Jan Eric Lenssen Matthias Fey Pascal Libuschewski TU Dortmund University Computer Graphics Group 44227 Dortmund, Germany {janeric.lenssen, matthias.fey, pascal.libuschewski}@udo.edu |
| Pseudocode | Yes | Algorithm 1 Group capsule layer |
| Open Source Code | Yes | For further details, we refer to our implementation, which is available on Github1. 1Implementation at: https://github.com/mrjel/group_equivariant_capsules_pytorch |
| Open Datasets | Yes | MNIST datasets [Le Cun et al., 1998]... standard MNIST dataset with 50k training examples and the dedicated MNIST-rot dataset with the 10k/50k train/test split [Larochelle et al., 2007]. In addition, we replicated the experiment of Sabour et al. [2017] on the aff NIST dataset2, a modification of MNIST where small, random affine transformations are applied to the images. |
| Dataset Splits | No | The paper mentions "MNIST-rot dataset with the 10k/50k train/test split" but does not explicitly detail a validation split or how it was used. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like "spline-based convolution operator" and the GitHub link implies PyTorch, but no specific version numbers for software dependencies are provided in the text. |
| Experiment Setup | Yes | Our canonical architecture consists of five capsule layers where each layer aggregates capsules from 2 2 spatial blocks with stride 2. ... The numbers of output capsules are 16, 32, 32, 64, and 10 per spatial position for each of the five capsule layers, respectively. In total, the architecture contains 235k trainable parameters (145k for the capsules and 90k for the CNN). ... We use the spread loss as proposed by Hinton et al. [2018] for the capsule part and standard cross entropy loss for the convolutional part and add them up. We trained our models for 45 epochs. |