Scale-Equivariant Steerable Networks
Authors: Ivan Sosnovik, Michał Szmaja, Arnold Smeulders
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate state-of-the-art results on the MNIST-scale dataset and on the STL-10 dataset in the supervised learning setting. In this section we conduct the experiments and compare various methods for working with scale variations in input data. |
| Researcher Affiliation | Collaboration | Ivan Sosnovik , Michał Szmaja , Arnold Smeulders Uv A-Bosch Delta Lab University of Amsterdam |
| Pseudocode | No | We illustrate all algorithms in Figure 1. |
| Open Source Code | Yes | Source code is available at http://github.com/isosnovik/sesn |
| Open Datasets | Yes | We conduct the experiments on the MNIST-scale dataset. We rescale the images of the MNIST dataset Le Cun et al. (1998)... |
| Dataset Splits | Yes | The obtained dataset is then split into 10,000 for training, 2,000 for evaluation and 50,000 for testing. |
| Hardware Specification | Yes | We used 1 Nvidia Ge Force GTX 1080Ti GPU for training the models. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | All models are trained with the Adam optimizer Kingma & Ba (2014) for 60 epochs with a batch size of 128. Initial learning rate is set to 0.01 and divided by 10 after 20 and 40 epochs. All models are trained for 1000 epochs with a batch size of 128. We use SGD optimizer with Nesterov momentum of 0.9 and weight decay of 5 10 4. The initial learning rate is set to 0.1 and divided by 5 after 300, 400, 600 and 800 epochs. |