Kernel Filtering Linear Overparameterization (KFLO)

The implementation in this repo was developed upon the ACNet code in the repo https://github.com/DingXiaoH/ACNet. Changes were made to implement and run KFLO.

Instructions

Use CUDA==10.2 and install the following packages in your python environment using the following commands:

pip install torch==1.3.0
pip install torchvision==0.4.1
pip install h5py
pip install tensorflow-gpu==1.15.0
pip install coloredlogs
pip install tqdm

For CIFAR-10 experiments follow the steps below, copied from https://github.com/DingXiaoH/ACNet excluding the method related content:

1. Enter this directory.

2. Make a soft link to your CIFAR-10 directory. If the dataset is not found in the directory, it will be automatically downloaded.

ln -s YOUR_PATH_TO_CIFAR cifar10_data

3. Set the environment variables.

export PYTHONPATH=.
export CUDA_VISIBLE_DEVICES=0

4. Train the Cifar-quick KFLO. Then train a regular model as baseline for the comparison.

python kflo/do_kflo.py -a cfqkbnc -b kflo
python kflo/do_kflo.py -a cfqkbnc -b base

5. Do the same on VGG.

python kflo/do_kflo.py -a vc -b kflo
python kflo/do_kflo.py -a vc -b base