Binary-Classification--Equivariant-Neural-Network

Models for Binary Classification using Equivariant Neural Network

Equivariant Neural Networks for Dark Matter Morphology with Strong Gravitational Lensing.

Use an Equivariant Neural Network to build a robust and efficient model for binary classification on the lensing dataset.

ROC curve (Receiver Operating Characteristic curve) and AUC score (Area Under the ROC Curve)

I have used General E(2)-Equivariant Steerable CNNs to implement the solution for the task.
Test_Cyclic.ipynb builds a model equivariant to 8 rotations. We indicate the group of N discrete rotations as C_N which means the Cyclic Group of order N. We implement C₈.
Test_Dihedral.ipynb builds a model based on Dihedral group D_2N which is group of N discrete planar rotations and reflections. D_N has an order of 2N. We implement D₄ in this notebook which expand to 4 rotations and 4 reflections.
Model Weights for Dihedral Group Equivariance : https://drive.google.com/file/d/1pgGjBwAUHTiXejiWA3hpHuHRB1BkIwEf/view?usp=sharing
Colab Notebook for Dihedral : https://colab.research.google.com/drive/1q6E-qFMwR7hxYfBbnV9YUZqR6VfAQDJ-?usp=sharing
Model Weights for Cyclic Group Equivariance : https://drive.google.com/file/d/1AKAGe-cecKObsnBrK76LTBHNqFwz1bOa/view?usp=sharing
Colab Notebook for Cyclic : https://colab.research.google.com/drive/1tHji-woLdPQsZFJ0ZD4NjD--GjtgFqwP?usp=sharing

Model	Architecture	Accuracy in % (on testing data)
C₈	C₈ Steerable CNN	99.6
D₄	D₄ Steerable CNN	99.4

Model	AUC Score
C₈	1.0
D₄	1.0