Learning from Small Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales

Code for ''Learning from Small Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales'' (NeurIPS 2022)

Prerequisites

Here we list our running environment:

• python == 3.7.13
• PyTorch == 1.12.1
• pytorch-lightning == 1.7.7
• torchvision == 0.13.1
• numpy == 1.21.6
• scipy == 1.7.3
• scikit-learn == 1.0.2
• emnist == 0.0
• matplotlib == 3.5.3
• tqdm == 4.64.1

Dataset

The ratio of training set and validation set is always 5:1. In addition, the number of test samples is always the entire test set, which is 10,000 for the MNIST/Transformed MNIST dataset and 20,800 for the EMNIST Letter dataset. The randomness of splits is fixed (random_state=4), while translated pixels and rotated degrees (transformed datasets) are random (need to run 5 times to calculate the average and standard deviation).

Training and testing

To run the experiments, simply execute the following commands,

python main.py