PLLay: Efficient Topological Layer based on Persistence Landscapes
This repository is the official implementation of PLLay: Efficient Topological Layer based on Persistence Landscapes.
Kwangho Kim, Jisu Kim, Manzil Zaheer, Joon Sik Kim, Frederic Chazal, Larry Wasserman
PLLay imports the following libraries:
This repository contains two experiments, one on MNIST dataset and another on ORBIT5K dataset, both with noise and corruption added. The experiment for MNIST dataset is under mnist directory, and the experiment for ORBIT5K dataset is under orbit5k directory. Each experiment consists of: generating data, preprocessing, training, evaluation, and training with evaluation for SLay. All the hyperparameters used in the experiments are specified in the python code.
This is to generate datasets and add noise and/or corruption. To generate data, run these commands:
This is to compute landscapes in advance. To preprocess data, run these commands:
To train the models using PLLay in the paper, run this command:
To evaluate the trained models using PLLay, in the paper, run this command:
Training and Evaluation for SLay
In the paper, we also considered SLay for the comparison. For SLay, we don't generate pre-defined models but we train and evaluate together. To train and evaluate models using SLay in the paper, run this command: