Deep Learing 2020 Course Project: Enhancing Interpretability of Self-Explaining Neural Networks

Introduction

This repository contains the code for reproducing the results of our project in the 2020 Deep Learning class at ETH Zurich. Our project is based on the paper "Towards Robust Interpretability with Self-Explaining Neural Networks" [1]. Please find our project report here.

Installing dependencies

pip install -r requirements.txt

Code structure

Folder	Filename	Description
SENN	aggregators.py	definitions of aggregation functions
SENN	conceptizers.py	definitions of functions that encode inputs into concepts
SENN	parametrizers.py	definitions of functions that generate relevances from inputs
SENN	encoders.py	definitions of encoders for conceptizers
SENN	decoders.py	definitions of decoders for conceptizers
SENN	losses.py	definitions of loss functions e.g., robustness (stability) loss
SENN	models.py	definition of model classes
SENN	backbones.py	definition of backbone models for parametrizers i.e., VGG net
SENN	utils.py	helper functions e.g., dataset definitions, custom layers
SENN	eval_utils.py	helper functions for evaluation
SENN	trainers.py	model training utilities
SENN	simsiam.py	definitions of siamese networks
SENN	argparser.py	argparser for different models
SENN	invarsenn.py	definition of InvarSENN modules
SENN	disentanglers.py	definition of disentangler for InvarSENN
Scripts	senn.py	script for reproduction of results for SENN
Scripts	vaesenn.py	script for reproduction of results for VaeSENN
Scripts	vsiamsenn.py	script for reproduction of results for V-SiamSENN
Scripts	invarsenn.py	script for training InvarSENN

Reproduction of results

Reproduction of results for SENN

## MNIST dataset 
python Scripts/senn.py --dataset "MNIST"
## CIFAR10 dataset
python Scripts/senn.py --dataset "CIFAR10" --n_epochs 200

Reproduction of results for VaeSENN

## MNIST dataset
python Scripts/vaesenn.py --dataset "MNIST"
## CIFAR10 dataset
python Scripts/vaesenn.py --dataset "CIFAR10" --n_epochs 200

Reproduction of results for VSiamSENN

## MNIST dataset
python Scripts/vaesenn.py --dataset "MNIST"
## CIFAR10 dataset
python Scripts/vaesenn.py --dataset "CIFAR10" --n_epochs 200

In order to load a pretraianed model please specify path via --path_pretrained [PATH] flag.

Additional model: InvarSENN

## MNIST dataset
python Scripts/invarsenn.py --dataset "MNIST"
## CIFAR10 dataset
python Scripts/invarsenn.py --dataset "CIFAR10" --n_epochs 200

Authors

• Edward Guenther (gedward@student.ethz.ch)
• Carina Schnuck (cschnuck@student.ethz.ch)
• Massimo Hoehn (hoehnm@student.ethz.ch)

References

[1] David Alvarez-Melis and Tommi S. Jaakkola. "Towards Robust Interpretability with Self-Explaining Neural Networks". 2018.