Introduction

This repository contains the implementaion for the paper:

Knowledge Distillation Inspired Fine-Tuning Of Tucker Decomposed CNNS and Adversarial Robustness Analysis Ranajoy Sadhukhan, Avinab Saha, Dr. Jayanta Mukhopadhyay, Dr. Amit Patra IEEE International Conference on Image Processing (ICIP) 2020

Data preparation

• Download train-val sets of Image-ILSVRC12 for experiments on ImageNet
• Update data path in run script

 conda env update -n tensordecomp -f environment.yml
 source activate tensordecomp
 pip install -e .

• Put cifar10 and cifar100 files cifar-10-python.tar.gz and cifar-100-python.tar.gz within same folder cifar

Installation

The training/testing environment can be initialized using conda as:

conda env update -n tensordecomp -f environment.yml
source activate tensordecomp
pip install -e .

Tensor Decomposition methods

There are two Tensor Decomposition methods implemented here

• CP Decomposition
• Tucker Decomposition

Adversarial Attacks

The original paper addresses DeepFool attack only. This repository extends experiments on two more adversarial attacks

• PGD attack
• Carlini-Wagner attack

We have used Foolbox for the implementations of these attacks

Running the code

Update the model and dataset information in TensorDecomp/config/default.py accordingly

cd TensorDecomp
chmod +x run.sh
./run.sh

For decomposing the network, use a pretrained undecomposed checkpoint Update run.sh script as follows

python main.py --pretrained --decompose --gpu <device_id>

Update architecture name and the type of loss function in the config file TensorDecomp/config/default.py In order to use logits loss or KL divergence loss for implementing Knowledge Distillation, Update run.sh script as follows

python main.py --pretrained --decompose --teacher --gpu <device_id>

Update _C.SOLVER.LOSS as 'L2' or 'L1' or 'KD' in TensorDecomp/config/default.py to implement appropriate loss function.