Steerable CNNs
Authors: Taco S. Cohen, Max Welling
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implemented steerable CNNs in Chainer (Tokui et al., 2015) and performed experiments on the CIFAR10 dataset (Krizhevsky, 2009) to determine if steerability is a useful inductive bias, and to determine the relative merits of the various types of capsules. |
| Researcher Affiliation | Academia | Taco S. Cohen University of Amsterdam t.s.cohen@uva.nl Max Welling University of Amsterdam Canadian Institute for Advanced Research m.welling@uva.nl |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper states 'We implemented steerable CNNs in Chainer (Tokui et al., 2015)' but does not provide a link or explicit statement about the availability of their own source code. |
| Open Datasets | Yes | We implemented steerable CNNs in Chainer (Tokui et al., 2015) and performed experiments on the CIFAR10 dataset (Krizhevsky, 2009) |
| Dataset Splits | No | The paper states 'we tuned the width (number of channels, K) using a validation set' but does not specify the size or percentage of the validation split (e.g., '80/10/10 split', 'X samples for validation'). |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., GPU/CPU models, memory, or specific computing infrastructure) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Chainer (Tokui et al., 2015)' but does not provide specific version numbers for Chainer or any other software dependencies. |
| Experiment Setup | No | The paper mentions aspects of the network architecture (e.g., '20 layer architecture', 'various widths') and states 'The rest of the training procedure is identical to Cohen & Welling (2016)', but does not provide specific hyperparameter values (e.g., learning rate, batch size, epochs) within its own text. |