Achieving Rotational Invariance with Bessel-Convolutional Neural Networks
Authors: Valentin Delchevalerie, Adrien Bibal, Benoît Frénay, Alexandre Mayer
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments are described and discussed in Section 5. Finally, a more general discussion about some aspects of B-CNNs, as well as some future works, are presented in Section 6 before concluding the paper in Section 7. |
| Researcher Affiliation | Academia | Valentin Delchevalerie,1 Adrien Bibal,2 Benoît Frénay,2 Alexandre Mayer3 1 PRe CISE, NADI & na Xys institutes, University of Namur, Belgium 2 PRe CISE, NADI institute, University of Namur, Belgium 3 Department of Physics, na Xys institute, University of Namur, Belgium {valentin.delchevalerie, adrien.bibal, benoit.frenay, alexandre.mayer}@unamur.be |
| Pseudocode | No | The paper describes the mathematical operations and shows a schematic illustration of the process (Figure 2), but it does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | The first dataset is MNIST [27]... The second dataset is Outex-TC-00010-r dataset [28]... The third dataset is made of 128 128 3 brain MRI images [29]. |
| Dataset Splits | No | For MNIST: "For all runs, the training set contains 60, 000 images and the test set 10, 000 images." For Outex: "The training set contains 480 images with 20 orientations, and the test set is composed of 3840 images with 160 orientations." For Brain MRI: "The training and test sets contain 190 and 63 images, respectively." While a validation set is mentioned for model selection ("by picking the one that performs the best on a validation set"), its specific size or proportion is not provided for any dataset, making the full split details incomplete for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., GPU models, CPU types, memory specifications). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, frameworks). |
| Experiment Setup | Yes | Adam optimization with a learning rate of 0.0001 and an exponential decay rate of 0.9 is used for the three models. ... Activation functions are relu, except for the B-CNN where tanh leads to better performances on the validation set. |